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A three-phase intelligent technique has been constructed to improve the data-hiding algorithm in colour images with imperceptibility. The first phase of the learning system (LS) has been applied in advance, whereas the other phases have been applied after the hiding process. The first phase has been constructed to estimate the number of bits to be hidden at each pixel (NBH); this phase is based on adaptive neural networks with an adaptive genetic algorithm using upwind adaptive relaxation (LS ANN _ AGAUpAR1 ). The LS of the second phase (LS ANN _ AGAUpAR2 ) has been introduced as a detector to check the performance of the proposed steganographic algorithm by creating a rich images model from available cover and stego images. The LS of the last phase (LS CANN _ AGAUpAR3 ) has been implemented through three steps, and it is based on a concurrent approach to improve the stego image and defend against attacks. The adaptive image filtering and adaptive image segmentation algorithms have been introduced to randomly hide a compressed and encrypted secret message into a cover image. The NBH for each pixel has been estimated cautiously using 32 principle situations (PS) with their 6 branch situations (BS). These situations have been worked through seven layers of security to augment protection from attacks. In this paper, hiding algorithms have been produced to fight three types of attacks: visual, structural, and statistical attacks. The simulation results have been discussed and compared with new literature using data hiding algorithms for colour images. The results of the proposed algorithm can efficiently embed a large quantity of data, up to 12 bpp (bits per pixel), with better image quality. [ABSTRACT FROM AUTHOR]

In this paper we present the modeling of a neuro-fuzzy system to be applied in the mix of paint from color model cyan, magenta and yellow (CMY). We aim to research an efficient method to get a specific hue color that is located within color wheel. The modeling is focused in to generate a wide gamma of colors to paint crafts like Panama Hats only with mentioned colors. Variables of the system are given by the frame and shape of selection of color. We use a fuzzy controller to generate the magnitude of the CMY percentages and a neural network to interpret the components to dosage the assigned color. Finally proceed data are used to develop an embedded controller under open source, the resulting color from the system is verified using different samples to determine the accuracy with selected color. [ABSTRACT FROM AUTHOR]

Object grasping by robot hands is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. To overcome these challenges, the essential purpose is to achieve the mathematical model of the robot hand, model the object and the contact between the object and the hand. In this paper, an intelligent hand-object contact model is developed for a coupled system assuming that the object properties are known. The control is simulated in the Matlab Simulink/SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural network to treat natural language. Most of the conventional neural networks can only process sentences consisted of a few words, and their applications are very simple such as metaphor understanding. The proposed network can process many complicated sentences and can be used as an associative memory and a question-answering system for factoid questions. The proposed network is composed of three layers and one network: Sentence Layer, Knowledge Layer, Deep Case Layer and Dictionary Network. The input sentences are divided into knowledge units and stored in the Knowledge Layer. The Deep Case Layer plays an important role in processing the knowledge units properly. The Dictionary Network also plays an important role as a knowledge base. We have carried out several experiments and they have shown that the proposed neural network has superior performances as an associative memory and a question-answering system. Especially as a question-answering system, the performance is very close to the elaborated system based on artificial intelligence. [ABSTRACT FROM AUTHOR]

Abstract: Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. [Copyright &y& Elsevier]

Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used to accomplish this step. However, the complexity of the neural network structure still stands up as a critical problem against prediction accuracy. In this paper, a method inspired by the proportional–integral–derivative (PID) control approach is investigated to enhance the performance of neural network models used for multi-step ahead prediction of periodic time-series information while maintaining a negligible impact on the complexity of the system. The PID-based method is applied to the predicted value at each time step to bring that value closer to the real value. The water demand forecasting problem is considered as a case study, where two deep neural network models from the literature are used to prove the effectiveness of the proposed boosting method. Furthermore, to prove the applicability of this PID-based booster to other types of periodic time-series prediction problems, it is applied to enhance the accuracy of a neural network model used for multi-step forecasting of hourly energy consumption. The comparison between the results of the original prediction models and the results after using the proposed technique demonstrates the superiority of the proposed method in terms of prediction accuracy and system complexity. [ABSTRACT FROM AUTHOR]

Abstract: Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. [Copyright &y& Elsevier]

Abstract: This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal methodology for the analysis, prediction and interpretation of urban growth. The proposed urban model takes into account the changes over time in population and building use patterns. A GIS is used for handling the spatial and temporal data, performing contingency analysis and mapping the results. Spatial entities with similar characteristics are grouped together in clusters by the use of a fuzzy c-means algorithm. Each cluster represents a specific level of urban growth and development. A two-layer feed-forward multilayer perceptron artificial neural network is then used to predict urban growth. The model, applied to the prefecture of Attica, Greece, delineates the current and future evolution trends of the Athens metropolitan area, which are illustrated by maps of the urban growth dynamics. The proposed methodology aims to assist planners and decision makers in gaining insight into the transition from rural to urban. [Copyright &y& Elsevier]

Abstract: Modelling complex dynamic mechanical systems, such as PEMFC, without any physical models is a difficult challenge but it could allow the monitoring of endurance tests of fuel cell systems. Neural networks are recognised as powerful numerical tools for predicting complex and nonlinear dynamic behaviours. They require only data limited to experimental inputs and outputs but the choice of an adapted architecture is critical. This paper presents a method for defining a neural network architecture optimised for the fuel cell systems. The associated experimental conditions specifying the vibration tests to train and validate were defined. They consist of swept sinus as well as random excitation forces. The resulting simulations are presented and analysed. [Copyright &y& Elsevier]

Abstract: This paper deals with one of the most important keys for economic growth: scientific knowledge and innovation, following the linear Research and Development (R&D) model. Patents, scientific publications and expenditure in R&D as well as the personnel involved in these activities are taken into account as proxy indicators, together with variables related to education and economy in order to classify R&D performance in 25 European Union (EU) Member States. This study classifies these countries using a set of variables which characterize them from 2005 to 2008 and analyses the most relevant ones for this classification. The Multilayer Perceptron Model (MLP) and the Product-Unit Neural Network (EPUNN) models, both trained by evolutionary algorithms (EA), were used to classify yearly country observations in clusters previously defined by employing unsupervised algorithm k-means clustering, obtaining four different classes of national R&D performance: low, moderate, high and innovation driven economies. Finally, our methodology is compared to other classification methods normally used in machine learning. The results show that while various methods of classification exist, our methodology obtains models with a significantly lower number of coefficients without decreasing their accuracy in predicting the classification of other European countries or in these countries in the following years. [Copyright &y& Elsevier]

Abstract: Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques. [Copyright &y& Elsevier]

Abstract: This paper investigates the performance of neural network-based techniques applied to the problem of defining the relationship between a particular type of variation in face images and the multivariate data distributions of these images. In this respect the problem of defining a mapping associating a quantified facial attribute and the overall typical facial appearance is addressed. In particular the applicability of formulating a mapping function using neural network-based methods like Multilayer Perceptrons (MLPs), Radial Basis Functions (RBFs), Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM) is investigated. Quantitative and visual results obtained during the experimental investigation, suggest that for one-to-many problems, where the entire variance of the distribution is not required, the RBFs are the best options when compared to MLPs, MDNs and GTM. The proposed techniques can be applied to applications involving face image synthesis and other applications that require one-to-many mapping transformations. [Copyright &y& Elsevier]

Abstract: Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961–December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models. [Copyright &y& Elsevier]

Abstract: In Multi-Task Learning (MTL), when several related tasks are learned at the same time considering one of them as the main task and the others as secondary ones, there is a transfer of positive information that improves the performance of the main one. However, not only does the difficulty of finding the relationship among different tasks pose a problem in real applications, but also knowing the kind of relationship among them. This paper presents a new method to generate artificial hints (subsets from the original data set) that helps the learning of the main task when all of them are learned simultaneously (as in a MTL scheme). Thus, although these hints cannot be strictly considered as secondary tasks, they will act as guides for the main one. The results obtained with toy and real problems show the advantages of the proposed method. In particular, a faster convergence, a very good performance, and a reduction in the likelihood of being trapped in a local minimum are achieved. [Copyright &y& Elsevier]

Abstract: In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill therefore they have to be estimated. Estimation of process variables, particularly temperature, is of crucial importance for the bar front section to fulfil quality requirements and it must be performed in the shortest possible time to keep heat. Currently, temperature estimation is performed by physical modelling, however it is highly affected by measurement uncertainties, variations in the incoming bar conditions and final product changes. In order to overcome these problems, artificial intelligence techniques as artificial neural networks and fuzzy logic have been proposed. In this paper, several neural networks, neural based Grey-Box models, fuzzy inference systems, and fuzzy based Grey-Box models are designed and tested with experimental data to estimate scale breaker entry temperature given the relevance of this variable. Their performances are compared against that of the physical model used in plant. Some of the systems presented in this work were proved to have better performance indexes and hence better prediction capabilities than the current physical models used in plant. [Copyright &y& Elsevier]

Abstract: In this paper, we present an abstract argumentation framework for the support of agreement processes in agent societies. It takes into account arguments, attacks among them, and the social context of the agents that put forward arguments. Then, we define the semantics of the framework, providing a mechanism to evaluate arguments in view of other arguments posed in the argumentation process. We also provide a translation of the framework into a neural network that computes the set of acceptable arguments and can be tuned to give more or less importance to argument attacks. Finally, the framework is illustrated with an example in a real domain of a water-rights transfer market. [Copyright &y& Elsevier]

Abstract: Employing the matrix measure approach and Lyapunov function, the author studies the global exponential stability of delayed neural networks with impulses in this paper. Some novel and sufficient conditions are given to guarantee the global exponential stability of the equilibrium point for such delayed neural networks with impulses. Finally, three examples are given to show the effectiveness of the results obtained here. [Copyright &y& Elsevier]

Abstract: The application of neural networks in the data mining has become wider. Although neural networks may have complex structure, long training time, and the representation of results is not comprehensible, neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. On the other hand, It is an open question as to what is the best way to train and extract symbolic rules from trained neural networks in domains like classification. In this paper, we train the neural networks by constructive learning and present the analysis of the convergence rate of the error in a neural network with and without threshold which have been learnt by a constructive method to obtain the simple structure of the network. The response of ANN is acquired but its result is not in understandable form or in a black box form. It is frequently desirable to use the model backwards and identify sets of input variable which results in a desired output value. The large numbers of variables and nonlinear nature of many materials models that can help finding an optimal set of difficult input variables. We will use a genetic algorithm to solve this problem. The method is evaluated on different public-domain data sets with the aim of testing the predictive ability of the method and compared with standard classifiers, results showed comparatively high accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, a new artificial neural network model is proposed for visual object recognition, in which the bottom-up, sensory-driven pathway and top-down, expectation-driven pathway are fused in information processing and their corresponding weights are learned based on the fused neuron activities. During the supervised learning process, the target labels are applied to update the bottom-up synaptic weights of the neural network. Meanwhile, the hypotheses generated by the bottom-up pathway produce expectations on sensory inputs through the top-down pathway. The expectations are constrained by the real data from the sensory inputs, which can be used to update the top-down synaptic weights accordingly. To further improve the visual object recognition performance, the multi-scale histograms of oriented gradients (MS-HOG) method is proposed to extract local features of visual objects from images. Extensive experiments on different image datasets demonstrate the efficiency and robustness of the proposed neural network model with features extracted using the MS-HOG method on visual object recognition compared with other state-of-the-art methods. [Copyright &y& Elsevier]

Abstract: A novel segment confidence-based binary segmentation (SCBS) for cursive handwritten words is presented in this paper. SCBS is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, SCBS is an unordered segmentation approach. SCBS is repetition of binary segmentation and fusion of segment confidence. Each repetition generates only one final segmentation point. The binary segmentation module is a contour tracing algorithm to find a segmentation path to divide a segment into two segments. A set of segments before binary segmentation is called pre-segments, and a set of segments after binary segmentation is called post-segments. SCBS uses over-segmentation technique to generate suspicious segmentation points on pre-segments. On each suspicious segmentation point, binary segmentation is performed and the highest fusion value is recorded. If the highest fusion value is greater than the one of pre-segments, the suspicious segmentation point becomes the final segmentation point for the iteration. If not, no more segmentation is required. Segment confidence is obtained by fusing mean character, lexical and shape confidences. The proposed approach has been evaluated on local and benchmark (CEDAR) databases. [Copyright &y& Elsevier]

Abstract: A new hybrid adaptive neural network (ANN) with modified adaptive smoothing errors (MASE) based on genetic algorithm (GA) employing modified adaptive relaxation (MAR) are presented in this paper to construct learning system for complex problem solving in fluid dynamics. This system can predict an incompressible viscous fluid flow represents by stream function (ψ) through symmetrical backward-facing steps channels. The proposed learning system is constructed as an intelligent computing technique by enforcing three stages run simultaneously; the first stage concerns to construct finite-element method (FEM) employing a new approach named modified adaptive incremental loading (MAIL) to build-up in run-time a dataset driven that contains an effective patterns represented by ψ for specific Reynolds number (Re), these patterns are associated to three kinds of clusters. The second stage is pertained a new hybrid neural network with new modification of adaptive smoothing errors and the third stage illustrated to modifying the numerical values of neural network connection weights through certain training algorithm with new optimization approach. The present simulation results of the proposed learning system are in good agreement with the available previous works and it is fast enough and stable. [Copyright &y& Elsevier]

Abstract: This paper reports the most recent development results of a simulation tool for assessment of radiation dose exposition by nuclear plant’s personnel, using artificial intelligence and virtual reality technologies. The main purpose of this tool is to support training of nuclear plants’ personnel, to optimize working tasks for minimisation of received dose. A finer grid of measurement points was considered within the nuclear plant’s room, for different power operating conditions. Further, an intelligent system was developed, based on neural networks, to interpolate dose rate values among measured points. The intelligent dose prediction system is thus able to improve the simulation of dose received by personnel. This work describes the improvements implemented in this simulation tool. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a hybrid artificial intelligent approach based on the clonal selection principle of artificial immune system (AIS) and neural networks is proposed to solve multi-objective programming problems. Due to the sensitivity to the initial values of initial population of antibodies (Ab’s), neural networks is used to initialize the boundary of the antibodies for AIS to guarantee that all the initial population of Ab’s is feasible. The proposed approach uses dominance principle and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the “not so good” antibodies. A secondary (or external) population that stores the nondominated solutions found along the search process is used. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the Pareto front. [ABSTRACT FROM AUTHOR]

Abstract: The classification of benign and malignant patterns in digital mammograms is one of most important and significant processes during the diagnosis of breast cancer as it helps detecting the disease at its early stage which saves many lives. Breast abnormalities are often embedded in and camouflaged by various breast tissue structures. It is a very challenging and difficult task for radiologists to correctly classify suspicious areas (benign and malignant patterns) in digital mammograms. In the early stage, the visual clues are subtle and varied in appearance, making diagnosis difficult; challenging even for specialists. Therefore, an intelligent classifier is required which can help radiologists in classifying suspicious areas and diagnosing breast cancer. This paper investigates a novel soft clustered based direct learning classifier which creates soft clusters within a class and learns using direct calculation of weights. The feature space for suspicious areas in digital mammograms from same class patterns can have multiple clusters and the proposed classifier uses this fact and introduces a novel idea to create soft clusters for each available class and applies them to form sub-classes within benign and malignant classes. A novel learning process based on direct learning is introduced. The experiments using the proposed classifier have been conducted on a benchmark database. The results have been analysed using ANOVA test which showed that the results are statistically significant. [Copyright &y& Elsevier]

Abstract: In this paper a new color space, called the RGB color ratio space, is proposed and defined according to a reference color such that an image can be transformed from a conventional color space to the RGB color ratio space. Because a color in the RGB color ratio space is represented as three color ratios and intensity, the chrominance can be completely reserved (three color ratios) and the luminance can be de-correlated with the chrominance. Different from traditional distance measurement, a road color model is determined by an ellipse area in the RGB ratio space enclosed by the estimated boundaries. A proposed adaptive fuzzy logic in which fuzzy membership functions are defined according to estimated boundaries is introduced to implement clustering rules. Therefore, each pixel will have its own fuzzy membership function corresponding to its intensity. A basic neural network is trained and used to achieve parameters optimization. The low computation cost of the proposed segmentation method shows the feasibility for real time application. Experimental results for road detection demonstrate the robustness to intensity variation of the proposed approach. [Copyright &y& Elsevier]

Abstract: Naturally fractured reservoirs are generally complex and highly heterogeneous. There are usually scarce data sources in these reservoirs that may be found over a wide range of scales. Therefore, fracture characterization is a complicated task and an integrated method is required to effectively overcome this multi-scaled problem and to combine data obtained from as many tools as possible. One sophisticated tool for this purpose is artificial intelligence. Recently, Fuzzy Logic and Neural Network have been used to obtain a 2D fracture intensity map in Hassi Messaoud field. The aim of this paper is to show the applicability of Artificial Neural Network and Fuzzy Logic in characterizing Parsi naturally fractured reservoir. First, using Fuzzy Inference System (FIS), Fracture Index (Fracture Intensity Index or FI) is calculated along the wellbore. For decreasing the uncertainty, using FIS, static and dynamic data (log, well test and core data) are coupled which results in a more reliable Fracture Index. Moreover, use of log data in FIS makes it possible to calculate the fractured index in those blocks where wells are perforated. Hence, a 3D characterization becomes possible. Then, for calculating the spatial distribution of the Fracture Index, two types of Neural Networks, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF), are used. The inputs of these networks are some geological and geomechanical drivers (e.g. shale volume, porosity, permeability, bed thickness, proximity to faults, slopes and curvatures of the structure), and the output is the Fracture Index. Because of the overfitting problem in MLP network, fuzzy ranking method is used for selecting only parts of the mentioned drivers as inputs of MLP network, and also a partially connected MLP network are used. RBF network produces more reasonable Fracture Index. Next, a 3D fracture intensity map in Parsi oil field is developed. According to this map, the high production rate of some wells can be explained in this field. Also, this 3D fracture intensity map can reduce the uncertainty in reservoir simulation. Therefore, a better field development scenario can be designed. The results are promising and can be easily extended in other naturally fractured reservoir. [Copyright &y& Elsevier]

Abstract: When network intruders launch attacks to a victim host, they try to avoid revealing their identities by indirectly connecting to the victim through a sequence of intermediary hosts, called stepping-stones. One effective stepping-stone detection mechanism is to detect such a long connection chain by estimating the number of stepping-stones. Artificial neural networks provide the potential to identify and classify network activities. In this paper, we propose an approach that utilizes the analytical strengths of neural networks to detect stepping-stone intrusion. Two schemes are developed for neural network investigation. One uses eight packet variables and the other clusters a sequence of consecutive packet round-trip times. The experimental results show that using neural networks as the detection tool works well to predict the number of stepping-stones for incoming packets by both proposed schemes through monitoring a connection chain with a few packets. In addition, various transfer functions and learning rules are studied and it is observed that using Sigmoid transfer function and Delta learning rule generally provides better prediction. [Copyright &y& Elsevier]

Abstract: This contribution describes the pattern recognition based data analysis of an existing industrial batch dryer, and the comparison of three artificial intelligence techniques suited to perform classification tasks: neural networks trained using the Levenberg–Marquardt and the Levenberg–Marquardt method with Bayesian regularization, the neuro-fuzzy model based on clustering and grid partition, and the Takagi–Sugeno fuzzy models. The classifiers are used to quantify the dryer batch time and its variation during a certain production period, thus the motivation behind the work is genuine. The presented pattern recognition method implements a supervised learning approach and is based on pressure measurement profiles recorded by the plant data management software—the PI System from OSIsoft. It is found that the neural networks trained with the Bayesian regularization have shown the most robust classification performance with respect to separation threshold selection. Furthermore, it is concluded that the application of artificial intelligence techniques in real chemical manufacturing facilities is feasible and provides useful information for process performance monitoring purposes. The pattern recognition findings presented in this paper are not case specific and can be directly used for the monitoring of a large variety of drying processes since the pressure profile features – vacuum check, pressure decrease, vacuum break – do not depend on the chemicals which are dried. Since the development of the artificial intelligent classifiers is presented in detail and step by step, this work may be interesting as a pattern recognition tutorial for chemical engineers. [Copyright &y& Elsevier]

Abstract: In this paper, the local coupled feedforward neural network is presented. Its connection structure is same as that of Multilayer Perceptron with one hidden layer. In the local coupled feedforward neural network, each hidden node is assigned an address in an input space, and each input activates only the hidden nodes near it. For each input, only the activated hidden nodes take part in forward and backward propagation processes. Theoretical analysis and simulation results show that this neural network owns the “universal approximation” property and can solve the learning problem of feedforward neural networks. In addition, its characteristic of local coupling makes knowledge accumulation possible. [Copyright &y& Elsevier]

Abstract: This paper presents the use of an intelligent hybrid stock trading system that integrates neural networks, fuzzy logic, and genetic algorithms techniques to increase the efficiency of stock trading when using a volume adjusted moving average (VAMA), a technical indicator developed from equivolume charting. For this research, a neuro–fuzzy-based genetic algorithm (NF-GA) system utilizing a VAMA membership function is introduced. The results show that the intelligent hybrid system takes advantage of the synergy among these different techniques to intelligently generate more optimal trading decisions for the VAMA, allowing investors to make better stock trading decisions. [Copyright &y& Elsevier]

Abstract: This paper presents an incremental learning algorithm for feed-forward neural networks used as approximators of real world data. This algorithm allows neural networks of limited size to be obtained, providing better performances. The algorithm is compared to two of the main incremental algorithms (Dunkin and cascade correlation) in the respective contexts of synthetic data and of real data consisting of radiation doses in homogeneous environments. [Copyright &y& Elsevier]

This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children’s mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child’s mathematical gift identified in previous research. The expert system estimated a child’s gift based on heuristically defined logic rules, while the scientifically confirmed psychological evaluation of gift based on Raven’s standard progressive matrices was used at the output of neural network models. Three neural network algorithms were tested on a Croatian dataset. The results show that both the expert system and the neural network recognize more pupils as mathematically gifted than teachers do. The expert system produces the highest average hit rate, although the highest accuracy in classifying gifted children is obtained by the radial basis neural network algorithm, which also yields lower type II error. Due to the ability of expert systems to explain the result, it can be suggested that both the expert system and the neural network model have potential to serve as effective intelligent decision support tools in detecting mathematical gift in early stage, therefore enabling its further development. [Copyright &y& Elsevier]

Abstract: Email is one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide, individuals and organizations more and more rely on the emails to communicate and share information and knowledge. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. It is becoming a big challenge to process and manage the emails efficiently for and individuals and organizations. This paper proposes new email classification models using a linear neural network trained by perceptron learning algorithm and a nonlinear neural network trained by back-propagation learning algorithm. An efficient semantic feature space (SFS) method is introduced in these classification models. The traditional back-propagation neural network (BPNN) has slow learning speed and is prone to trap into a local minimum, so the modified back-propagation neural network (MBPNN) is presented to overcome these limitations. The vector space model based email classification system suffers from a large number of features and ambiguity in the meaning of terms, which will lead to sparse and noisy feature space. So we use the SFS to convert the original sparse and noisy feature space to a semantically richer feature space, which will helps to accelerate the learning speed. The experiments are conducted based on different training set size and extracted feature size. Experimental results show that the models using MBPNN outperform the traditional BPNN, and the use of SFS can greatly reduce the feature dimensionality and improve email classification performance. [Copyright &y& Elsevier]

Abstract: In this paper, a dual neural network, LVI (linear variational inequalities)-based primal-dual neural network and simplified LVI-based primal-dual neural network are presented for online repetitive motion planning (RMP) of redundant robot manipulators (with a four-link planar manipulator as an example). To do this, a drift-free criterion is exploited in the form of a quadratic performance index. In addition, the repetitive-motion-planning scheme could incorporate the joint physical limits such as joint limits and joint velocity limits simultaneously. Such a scheme is finally reformulated as a quadratic program (QP). As QP real-time solvers, the aforementioned three kinds of neural networks all have piecewise-linear dynamics and could globally exponentially converge to the optimal solution of strictly-convex quadratic-programs. Furthermore, the neural-network based RMP scheme is simulated based on a four-link planar robot manipulator. Computer-simulation results substantiate the theoretical analysis and also show the effective remedy of the joint angle drift problem of robot manipulators. [Copyright &y& Elsevier]

Abstract: Neural networks (NNs) represent a familiar artificial intelligence approach widely applied in many fields and to a wide range of issues. The back propagation network (BPN) is one of the most well-known NNs, comprising multilayer perceptrons (MLPs) with an error back propagation learning algorithm. BPN typically employs associate multiplicative weightings for layer connections. For single connections, BPN combines neuron inputs linearly to neuron outputs. In this study, the author develops and embeds high order connections (exponent multipliers) into the BPN. The resultant proposed hybrid high order neural network (HHONN) is intended to be applicable to both linear and high order connections. HHONN allows an additional connection type for BPN, which permits BPN to adapt to different scenarios. In this paper, learning equations for both weighting and high order connections are introduced in their general forms. A feedforward neural network with a topology of two hidden layers and one high order connection was developed and studied to confirm the improved performance of developed HHONN models. Case studies, including two basic tests (a function approximation and the TC problem) and squat wall strength learning, were used to verify HHONN performance. Results showed that, when the high order connection was employed anywhere except the eventual connection, HHONN delivered better results than achievable using traditional BPN. Such results show that HHONN successfully introduces high order connections into BPN. [Copyright &y& Elsevier]

Abstract: Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180days. For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK), silica fume (SF), water, sand, aggregate and superplasticizer. According to these input parameters, in the multilayer feed forward neural networks models are predicted the compressive strength values of concretes containing metakaolin and silica fume. The training and testing results in the neural network models have shown that neural networks have strong potential for predicting 1, 3, 7, 28, 56, 90 and 180days compressive strength values of concretes containing metakaolin and silica fume. [Copyright &y& Elsevier]

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis. [Copyright &y& Elsevier]

The objective of this paper is to develop and test a model of cost estimating for the shell and tube heat exchangers in the early design phase via the application of artificial neural networks (ANN). An ANN model can help the designers to make decisions at the early phases of the design process. With an ANN model, it is possible to obtain a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of a shell and tube heat exchangers. [Copyright &y& Elsevier]

Abstract: This paper presents an overview of a data analysis method based on self-organizing maps (SOM), a well-known unsupervised neural network learning algorithm, which was applied to a lead-free wave soldering process. The aim of the study was to determine whether the neural network modeling method could be a useful and time-saving way to analyze data from a discrete manufacturing process, such as wave soldering, which is a widely used technique in the electronics industry to solder components on printed circuit boards. The data variables were mostly various process parameters, but also some solder defect numbers were present in the data as a measure of the product quality. The data analysis procedure went as follows. At first, the process data were modeled using the SOM-algorithm. Next, the neuron reference vectors of the formed map were clustered to reveal the desired dominating elements of each territory of the map. At the final stage, the clusters were utilized as sub-models to indicate variable dependencies in these sub-models. The results show that the method presented here can be a good way to analyze this type of process data because interesting interactions between certain process parameters and solder defects were found by means of this data-driven modeling method. [Copyright &y& Elsevier]

Abstract: A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods. [Copyright &y& Elsevier]

Abstract: Software engineering development has gradually become essential element in different aspects of the daily life and an important factor in numerous critical real-industry applications, such as, nuclear plants, medical monitoring control, real-time military, bioinformatics, oil and gas industry, and air traffic control. This paper proposes a functional network as a novel computational intelligence scheme for tracking and predicting the software reliability. Several applications are presented to illustrate this new intelligent system framework models. To demonstrate the usefulness of functional networks and the existing data mining schemes, we briefly describe the learning algorithm of functional networks associativity model in predicting the software reliability. Comparative studies will be carried out to compare the performance of functional networks with the most popular existing data mining techniques, such as, statistical regression multilayer feed forward neural networks, and support vector machines. The results show that the performance of functional networks is more reliable, stable, accurate, and outperforms other techniques. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to the tracking of multiple objects in CCTV surveillance using a combination of simple neural cost functions based on Self-Organizing Maps, and a greedy assignment algorithm. Using a reference standard data set and an exhaustive search algorithm for benchmarking, we show that the cost function plays the most significant role in realizing high levels of performance. The neural cost function’s context-sensitive treatment of appearance, change of appearance and trajectory yield better tracking than a simple, explicitly designed cost function. The algorithm matches 98.8% of objects to within 15 pixels. [Copyright &y& Elsevier]

Abstract: Software reliability is an important factor for quantitatively characterizing software quality and estimating the duration of software testing period. Traditional parametric software reliability growth models (SRGMs) such as nonhomogeneous Poisson process (NHPP) models have been successfully utilized in practical software reliability engineering. However, no single such parametric model can obtain accurate prediction for all cases. In addition to the parametric models, non-parametric models like neural network have shown to be effective alternative techniques for software reliability prediction. In this paper, we propose a non-parametric software reliability prediction system based on neural network ensembles. The effects of system architecture on the performance are investigated. The comparative studies between the proposed system with the single neural network based system and three parametric NHPP models are carried out. The experimental results demonstrate that the system predictability can be significantly improved by combing multiple neural networks. [Copyright &y& Elsevier]

Abstract: Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using the trust, negotiation, and communication (TNC) reasoning model. The main contribution of this work is that a novel trust measurement method, based on the recognition and rejection rates, is proposed. Besides, an auctioning procedure, based on the sealed bid, first price method, is adapted for the negotiation phase. Two agent teams are formed; each consists of three NN learning agents. The first is a fuzzy min–max (FMM) NN agent team and the second is a fuzzy ARTMAP (FAM) NN agent team. Modifications to the FMM and FAM models are also proposed so that they can be used for trust measurement in the TNC model. To assess the effectiveness of the proposed model and the bond (based on trust), five benchmark data sets are tested. The results compare favorably with those from a number of classification methods published in the literature. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the state estimation problem for a class of Markovian neural networks with discrete and distributed time-delays. The neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov chain. The main purpose is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally asymptotically stable in the mean square. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. Both the existence conditions and the explicit characterization of the desired estimator are derived. Furthermore, it is shown that the traditional stability analysis issue for delayed neural networks with Markovian jumping parameters can be included as a special case of our main results. Finally, numerical examples are given to illustrate the applicability of the proposed design method. [Copyright &y& Elsevier]

Abstract: This paper is a study of the application of artificial neural networks to the problem of calculating error compensation values for axis positioning on a machine tool. The primary focus is on the development of a neural network-based system that could be implemented and integrated into the open architecture control system of an actual machine. A number of neural network architectures were examined for applicability to the problem and one was selected and implemented on the actual machine. Positioning error compensation capabilities were evaluated using industry standard equipment and procedures, and the results obtained were compared with the capabilities of standard error compensation routines in machine tool controls. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of exponential stability of uncertain cellular neural networks with discrete and distribute time-varying delays is considered. Based on the Lyapunov function method and convex optimization approach, a new delay-dependent stability criterion with an exponential convergence rate is derived in terms of LMI (linear matrix inequality). Three numerical examples are given to show the effectiveness of the proposed methods. [Copyright &y& Elsevier]

Abstract: The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation. [Copyright &y& Elsevier]

Abstract: This paper proposes a GRG (Greedy Rule Generation) algorithm, a new method for generating classification rules from a data set with discrete attributes. The algorithm is “greedy” in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples and the size of subspaces that it covers, as well as the number of attributes in the rule. This method is employed for extracting rules from neural networks that have been trained and pruned for solving classification problems. The classification rules are extracted from the neural networks using the standard decompositional approach. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our experimental results show that neural network rule extraction with the GRG method produces rule sets that are accurate and concise. Application of GRG directly on three medical data sets with discrete attributes also demonstrates its effectiveness for rule generation. [Copyright &y& Elsevier]