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Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields. [ABSTRACT FROM AUTHOR]

It is a common practice to adjust the number of hidden neurons in training, and the removal of neurons in neural networks plays an indispensable role in this architecture manipulation. In this paper, a succinct and unified mathematical form is upgraded to the generic case for removing neurons based on orthogonal projection and crosswise propagation in a feedforward layer with different architectures of neural networks, and further developed for several neural networks with different architectures. For a trained neural network, the method is divided into three stages. In the first stage, the output vectors of the feedforward observation layer are classified to clusters. In the second stage, the orthogonal projection is performed to locate a neuron whose output vector can be approximated by the other output vectors in the same cluster with the least information loss. In the third stage, the previous located neuron is removed and the crosswise propagation is implemented in each cluster. On accomplishment of the three stages, the neural network with the pruned architecture is retrained. If the number of clusters is one, the method is degenerated into its special case with only one neuron being removed. The applications to different architectures of neural networks with an extension to the support vector machine are exemplified. The methodology supports in theory large-scale applications of neural networks in the real world. In addition, with minor modifications, the unified method is instructive in pruning other networks as far as they have similar network structure to the ones in this paper. It is concluded that the unified pruning method in this paper equips us an effective and powerful tool to simplify the architecture in neural networks. [ABSTRACT FROM AUTHOR]

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results. [ABSTRACT FROM AUTHOR]

In-band full duplex communication requires precise self-interference cancellation (SIC) to successfully decode the received desired signal. The existing neural network (NN) based SIC scheme uses offline trained NN to estimate the non-linear component of received self-interference (SI) over a static SI channel without additional NN training. For the time-varying SI channel, the NN-aided SIC method needs to retrain the NN during in-band full duplex communication to adapt time-varying channels. However, NN training is not fast enough to be done during full duplex communication. Thus, the SIC performance degrades. In this paper, we propose a digital SIC scheme using channel robust NN which takes estimates of linear SI channel coefficients as an input. It was found that the NN could learn the static part of the non-linear behavior of the SI channel well. Our solution can adapt the time-varying SI channel with only the channel coefficient estimation of linear components and a pre-trained NN. The proposed scheme can successfully reduce the SI to the noise floor for both time-invariant and time-varying SI channels. [ABSTRACT FROM AUTHOR]

Background: In this paper we present a model of parameters to aesthetically characterize films using a multi-disciplinary approach: by combining film theory, visual low-level video descriptors (modeled in order to supply aesthetic information) and classification techniques using machine and deep learning. Methods: Four different tests have been developed, each for a different application, proving the model's usefulness. These applications are: aesthetic style clustering, prediction of production year, genre detection and influence on film popularity. Results: The results are compared against high-level information to determine the accuracy of the model to classify films without knowing such information previously. The main difference with other film characterization approaches is that we are able to isolate the influence of high-level descriptors to really understand the relevance of low-level features and, accordingly propose a useful set of low-level visual descriptors for that purpose. This model has been tested with a representative number of films to prove that it can be used for different applications. [ABSTRACT FROM AUTHOR]

This paper presents a multi-resolution modeling approach using wavelet neural networks. A lithium battery model, which has three resolutions, is developed to depict the modeling approach. By combining the advantages of dyadic activation functions and the orthonormal property of wavelet functions, the developed battery model possesses two salient features. First, the model is built from a coarser approximation to a finer representation by adding more details incrementally. Second, the model at a low resolution is compatible with the model at a high resolution, which means that the parameters used in a low resolution can be directly incorporated into a high resolution without any modification. This paper's results show that this battery modeling provides great flexibility for users to choose a suitable resolution to meet their requirements for model accuracy and model execution speed. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of exponential stability is investigated for a class of stochastic neural networks with both Markovian jump parameters and mixed time delays. The jumping parameters are modeled as a continuous-time finite-state Markov chain. Based on a Lyapunov–Krasovskii functional and the stochastic analysis theory, a linear matrix inequality (LMI) approach is developed to derive some novel sufficient conditions, which guarantee the exponential stability of the equilibrium point in the mean square. The proposed LMI-based criteria are quite general since many factors, such as noise perturbations, Markovian jump parameters, and mixed time delays, are considered. In particular, the mixed time delays in this paper synchronously consist of constant, time-varying, and distributed delays, which are more general than those discussed in the previous literature. In the latter, either constant and distributed delays or time-varying and distributed delays are only included. Therefore, the results obtained in this paper generalize and improve those given in the previous literature. Two numerical examples are provided to show the effectiveness of the theoretical results and demonstrate that the stability criteria used in the earlier literature fail. [ABSTRACT FROM AUTHOR]

In this paper, modeling and identification of nonlinear dynamic systems using neuro-fractional Hammerstein model are considered. The proposed model consists of the neural networks (NNs) as the nonlinear subsystem and the fractional-order state space (FSS) as the linear subsystem. The identification procedure consists of a hybrid frequency-/time-domain approach based on the input-output data acquired from the system. First in the frequency domain, the fractional order and fractional degree of the FSS subsystem are determined offline using an iterative linear optimization algorithm. Then, in the time domain, the state-space matrices of the FSS as well as parameters of the NN are estimated using Lyapunov stability theory. Moreover, in order to use only the input-output data from the system, a fractional-order linear observer based on auxiliary model idea is utilized to estimate the system states. The convergence and stability analysis of the proposed method are provided. Simulating and experimental examples show superior performance of the proposed method as compared with the Hammerstein models reported in the literature. [ABSTRACT FROM AUTHOR]

Automatic image annotation aims to predict labels for images according to their semantic contents and has become a research focus in computer vision, as it helps people to edit, retrieve and understand large image collections. In the last decades, researchers have proposed many approaches to solve this task and achieved remarkable performance on several standard image datasets. In this paper, we propose a novel learning to rank approach to address image auto-annotation problem. Unlike typical learning to rank algorithms for image auto-annotation which directly rank annotations for image, our approach consists of two phases. In the first phase, neural ranking models are trained to rank image’s semantic neighbors. Then nearest-neighbor based models propagate annotations from these semantic neighbors to the image. Thus our approach integrates learning to rank algorithms and nearest-neighbor based models, including TagProp and 2PKNN, and inherits their advantages. Experimental results show that our method achieves better or comparable performance compared with the state-of-the-art methods on four challenging benchmarks including Corel5K, ESP Games, IAPR TC-12 and NUS-WIDE. [ABSTRACT FROM AUTHOR]

This article addresses why the neural network model has been selected by nature against other computational models to generate behavior in complex multicellular clades. This question, which has not yet been addressed in research, should not be ignored because understanding this issue is necessary to have a complete picture of the evolutionary process of the nervous system. The starting point to discuss the issue is a proposal made 30 years ago: the free-moving hypothesis. This proposal establishes prediction as the main function of the brain and that all multicellular organisms that move require a brain in order to make predictions. This article contains a review contrasting this hypothesis with the discoveries made in the last 30 years within different biological kingdoms. Although none of these discoveries contradict the free-moving hypothesis, it still does not answer the main question. Alternative hypotheses about the origin of the nervous system are discussed in this paper, but they also are not able to answer the question. Six hypotheses are proposed as possible answers, and each of them is discussed by comparing neural processing systems with three other alternative processing systems. The result is that the neural processing system is selected against other kinds of processing systems because it has computational robustness to damage, allowing its function of prediction to be more durable. While this result, called the first neural processing principle, answers the initial question and permits a finished proof of the free-moving hypothesis, it gives rise to the question of how computationally robust a system must be to be selected by nature. This paper claims that the system selected must be computationally robust enough to have enough offspring to allow variation. This answer, named the second neural principle, determines the minimum amount of neurons that a neural processing system must have, but not the maximum. To address this issue, the third and fourth neural processing principles are stated, which determine that the maximum number of neurons is limited by energetic restrictions and body size, respectively. The results presented in this paper show that computational robustness is an important parameter to understand the evolution of nervous system. [ABSTRACT FROM AUTHOR]

In this paper, the robust trajectory tracking design of uncertain nonlinear systems is investigated by virtue of a self-learning optimal control formulation. The primary novelty lies in that an effective learning based robust tracking control strategy is developed for nonlinear systems under a general uncertain environment. The augmented system construction is performed by combining the tracking error with the reference trajectory. Then, an improved adaptive critic technique, which does not depend on the initial stabilizing controller, is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with respect to the nominal augmented system. Using the obtained control law, the closed-loop form of the augmented system is built with stability proof. Moreover, the robust trajectory tracking performance is guaranteed via Lyapunov approach in theory and then through simulation demonstration, where an application to a practical spring–mass–damper system is included. [ABSTRACT FROM PUBLISHER]

Deep learning based land cover classification algorithms have recently been proposed in the literature. In hyperspectral images (HSIs), they face the challenges of large dimensionality, spatial variability of spectral signatures, and scarcity of labeled data. In this paper, we propose an end-to-end deep learning architecture that extracts band specific spectral-spatial features and performs land cover classification. The architecture has fewer independent connection weights and thus requires fewer training samples. The method is found to outperform the highest reported accuracies on popular HSI data sets. [ABSTRACT FROM PUBLISHER]

Two coupled reaction-diffusion neural networks (CRDNNs) with different dimensions of input and output are considered in this paper. The only difference between them is whether time-varying delay is incorporated in the mathematical model of network. We respectively analyze dissipativity and passivity of these CRDNNs. First, for the systems with different dimensions of input and output vectors, two new passivity definitions are proposed. Then, by exploiting some inequality techniques, several dissipativity and passivity criteria for these CRDNNs are established. Furthermore, we analyze stability of passive CRDNNs. Finally, two examples with simulation results are presented to verify the effectiveness of the proposed criteria. [ABSTRACT FROM PUBLISHER]

This paper examines a linked simulation-optimization procedure based on the combined application of an artificial neural network (ANN) and genetic algorithm (GA) with the aim of developing an efficient model for the multiobjective management of groundwater lenses in small islands. The simulation-optimization methodology is applied to a real aquifer in Kish Island of the Persian Gulf to determine the optimal groundwater-extraction while protecting the freshwater lens from seawater intrusion. The initial simulations are based on the application of SUTRA, a variable-density groundwater numerical model. The numerical model parameters are calibrated through automated parameter estimation. To make the optimization process computationally feasible, the numerical model is subsequently replaced by a trained ANN model as an approximate simulator. Even with a moderate number of input data sets based on the numerical simulations, the ANN metamodel can be efficiently trained. The ANN model is subsequently linked with GA to identify the nondominated or Pareto-optimal solutions. To provide flexibility in the implementation of the management plan, the model is built upon optimizing extraction from a number of zones instead of point-well locations. Two issues are of particular interest to the research reported in this paper are: (1) how the general idea of minimizing seawater intrusion can be effectively represented by objective functions within the framework of the simulation-optimization paradigm, and (2) the implications of applying the methodology to a real-world small-island groundwater lens. Four different models have been compared within the framework of multiobjective optimization, including (1) minimization of maximum salinity at observation wells, (2) minimization of the root mean square (RMS) change in concentrations over the planning period, (3) minimization of the arithmetic mean, and (4) minimization of the trimmed arithmetic mean of concentration in the observation wells. The latter model can provide a more effective framework to incorporate the general objective of minimizing seawater intrusion. This paper shows that integration of the latest innovative tools can provide the ability to solve complex real-world optimization problems in an effective way. [ABSTRACT FROM AUTHOR]

The most difficult part of an artificial neural network to implement in hardware is the nonlinear activation function. For most implementations, the function used is the hyperbolic tangent. This function has received much attention in relation to hardware implementation. Nevertheless, there is no consensus regarding the best solution. In this paper, we propose a new approach by implementing the hyperbolic tangent in hardware with a polynomial modeling of the fractional exponential part. The results in the paper then demonstrate, through the use of an example, that this solution is faster than the CORDIC algorithm, but slower than the piecewise linear solution with the same error. The advantage over the piecewise linear approach is that it uses less memory. [ABSTRACT FROM AUTHOR]

This paper studies neural network models of vehicle dynamics. We consider both models with a generic layer architecture and models with specialised topologies that hard-wire physics principles. Network pre-wiring is limited to universal laws; hence it does not limit the network modelling abilities on one side but allows more robust and interpretable models on the other side. Four different network types (with and without pre-wired structure, recursive and non-recursive) are compared for the longitudinal dynamics of a car with gears and two controls (brake and engine). Results show that pre-wiring effectively improves the performance. Non-recursive networks also look to be preferable for several reasons. [ABSTRACT FROM AUTHOR]

Short-term load forecasting for small power systems, namely zonal STLF, is one of the issues in the load forecasting area which has vital value for electricity market competitors like retailers and ISO. This paper discusses the common problems related to short term load forecasting which are particularly raised in the small power systems. Identifying bad data which constitute a large portion of load data in these systems, choosing a proper manner for selecting ANN training data, and requirements which should be considered in the structure of zonal STLF to facilitate its employing to different power systems are investigated in this paper. Here, a comprehensive model which considers these problems as well as the generality of structure and the simplicity of implementation is proposed. This model is tested on Isfahan power system which is one of the regional power companies of Iran national power system. The results show that this structure can be implemented effectively and have an acceptable error in spite of large amount of bad data among historical data. [ABSTRACT FROM AUTHOR]

One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes. [ABSTRACT FROM AUTHOR]

A radial basis function (RBF) neural network control algorithm can effectively improve the robotic manipulators’ performance against a large amount of uncertainty. The adaptive law can be derived by using the Lyapunov method so that the stability of robotic manipulator control system and the weight self-adaptive convergence of RBF neural networks will be guaranteed. Meanwhile, system fluctuations and even overshot phenomenon under every start-up process, which are caused by the system’s convergence from the given nonoptimal initial weight value to the optimal weight value, can be avoided by using memristors to remember the optimal weight after the system’s first operation. According to the above analysis, this correspondence paper designs a kind of RBF neural network control algorithm based on spintronic memristors, and then analyzes its theoretical derivation process and core design idea. Finally, the system simulation model, which uses a two-link robotic manipulator as control object, is built to prove the algorithm’s validity and feasibility. Simulation results show that the proposed algorithm can satisfy the effect of presupposition. [ABSTRACT FROM PUBLISHER]

Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework of a deep neural network (DNN) that improves the accuracy of satellite precipitation products, focusing on reducing the bias and false alarms. The state-of-the-art deep learning techniques developed in the area of machine learning specialize in extracting structural information from a massive amount of image data, which fits nicely into the task of retrieving precipitation data from satellite cloud images. Stacked denoising autoencoder (SDAE), a widely used DNN, is applied to perform bias correction of satellite precipitation products. A case study is conducted on the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) with spatial resolution of 0.08° × 0.08° over the central United States, where SDAE is used to process satellite cloud imagery to extract information over a window of 15 × 15 pixels. In the study, the summer of 2012 (June-August) and the winter of 2012/13 (December-February) serve as the training periods, while the same seasons of the following year (summer of 2013 and winter of 2013/14) are used for validation purposes. To demonstrate the effectiveness of the methodology outside the study area, three more regions are selected for additional validation. Significant improvements are achieved in both rain/no-rain ( R/NR) detection and precipitation rate quantification: the results make 33% and 43% corrections on false alarm pixels and 98% and 78% bias reductions in precipitation rates over the validation periods of the summer and winter seasons, respectively. [ABSTRACT FROM AUTHOR]

Non-linear processes like conical tank control system is complex because of its non-linear characteristics, long-term interval and time difference between the system input and output. In this context, neural network based controller works since it is able to control and train the non-linear data set of liquid level in order to optimize the network performance. Hence, this article proposes a neural network control using gradient descent with adaptive learning rate that improves the performance and minimizes the errors, by using moving average filter and Hanning window to enhance the non-linear data. The article mainly deals with an application involving ARMA and artificial neural-based network (ANN) to model a conical tank system. To remove the recurrent components and to predict the future values of the process, the present paper employs an Autoregressive Moving Average Model (ARMA) by identifying its time varying parameters and combining with artificial neural network. MATLAB R2016b was applied for the entire simulation and training of non-linear data set. The simulation results indicate a minimization in the difference between the net input to the output and target value with that of error. The results indicated that the simulation took only 13 s to train the entire network for 6,135 iterations with the ARMA based model. [ABSTRACT FROM AUTHOR]

Under circumstances where data quality may vary (due to inaccuracies or lack of timeliness, for example), knowledge about the potential performance of alternate predictive models can help a decision maker to design a business-value-maximizing information system. This paper examines a real-world example from the field of finance to illustrate a comparison of alternative modeling tools. Two modeling alternatives are used in this example: regression analysis and neural network analysis. There are two main results: (1) Linear regression outperformed neural nets in terms of forecasting accuracy, but the opposite was true when we considered the business value of the forecast. (2) Neural net-based forecasts tended to be more robust than linear regression forecasts as data accuracy degraded. Managerial implications for financial risk management of mortgage-backed security portfolios are drawn from the results. [ABSTRACT FROM AUTHOR]

Many animals rely on visual motion to infer their relation to the surrounding environment while moving around in it. Motion processing in biological nervous systems, particularly in the insects, has been a subject of active research for over 50 years. With the advent of interest in “neuromorphic” integrated circuits in the late 1980s, this mode of sensory processing has also been the subject of various analog silicon modeling efforts. The author discusses the background of certain models for motion detection in insects, and then the implementation of a particular model in analog integrated circuitry. The paper does not focus on such circuits themselves, however, but on the issue of computational precision in the analog domain: it is argued that insufficient attention to this issue has been an impediment to the development of neuromorphic circuits that are practically useful in engineering applications. A statistical approach is described for assessing one of the designs discussed herein, and the degradation of its performance due to variations in the electrical properties of its constituent devices, with an eye toward suitability for application in autonomous robotics. This approach relies on tools and models routinely used in conventional analog/mixed signal design. The author argues that adoption of such techniques, along with a deeper consideration of computational precision, i.e., a shift in the neuromorphic design philosophy, would be an important step in moving the field forward. [ABSTRACT FROM PUBLISHER]

One of the main limitations of artificial neural networks (ANN) is their high inability to know in an explicit way the relations established between explanatory variables (input) and dependent variables (output). This is a major reason why they are usually called 'black boxes.' In the last few years, several methods have been proposed to assess the relative importance of each explanatory variable. Nevertheless, it has not been possible to reach a consensus on which is the best-performing method. This is largely due to the different relative importance obtained for each variable depending on the method used. This importance also varies with the designed network architecture and/or with the initial random weights used to train the ANN. This paper proposes a procedure that seeks to minimize these problems and provides consistency in the results obtained from different methods. Essentially, the idea is to work with a set of neural networks instead of a single one. The proposed procedure is validated using a database collected from a customer satisfaction survey, which was conducted on the public transport system of Granada (Spain) in 2007. The results show that, when each method is applied independently, the variable's importance rankings are similar and, in addition, coincide with the hierarchy established by researchers who have applied other techniques. [ABSTRACT FROM AUTHOR]

In this paper, a methodology for the generation of optimal input signals for incremental data-based modelling of nonlinear static and dynamic systems is presented. For this purpose, an online strategy consisting of an evolving model and an iterative finite horizon input optimization in parallel to the ongoing experiment is pursued. Such an integrated methodology is methodically very efficient since the experiment is only conducted until the desired model quality is obtained. For the process excitation, the compliance with system input and output limits is of great importance. Especially for nonlinear dynamic systems, the compliance with output constraints is challenging since the current input has an impact on all forthcoming outputs. The generation of optimal inputs is based on the iterative optimization of the Fisher information matrix of the current process model subject to input and output constraints. For the identification, an evolving local model network is used that can cope with a growing amount of data. To this end, the parameter adaptation and the automated structure evolution are characteristic of the evolving local model network. The effectiveness of the proposed method is demonstrated on two typical automotive application examples. First, a stationary smoke model of a diesel engine and second, a dynamic exhaust temperature model are identified by use of optimal process excitation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the synchronization problem of impulsive fractional-order neural networks with both time-varying and distributed delays. By using the fractional Lyapunov method and Mittag–Leffler function, some sufficient conditions are derived to realize the global Mittag–Leffler synchronization of impulsive fractional-order neural networks and one illustrative example is given to demonstrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design. [ABSTRACT FROM AUTHOR]

This paper addresses the nonlinear identification of liquid saturated steam heat exchanger (LSSHE) using artificial neural network model. Heat exchanger is a highly nonlinear and non-minimum phase process and often its working conditions are variable. Experimental data obtained from fluid outlet temperature measurement in laboratory environment is used as the output variable and the rate of change of fluid flow into the system as input too. The results of identification using neural network and conventional nonlinear models are compared together. The simulation results show that neural network model is more accurate and faster in comparison with conventional nonlinear models for a time series data because of the independence of the model assignment. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid neural network (NN)-genetic algorithm (GA) based non-destructive pavement auscultation method for instantaneous airfield infrastructure condition assessment is discussed. NNs are employed for finite element aided forward prediction of pavement surface deflections resulting from non-destructive test impulse loading and the GAs are used for global optimisation of the pavement structural parameters by matching the NN predicted deflections with the measured pavement response. This hybrid approach takes advantage of the non-linear estimation capability provided by neural networks trained using finite element (FE) solutions in modelling the stress-dependent behaviour of unbound pavement geo-materials while improving the robustness to measurement uncertainty through the application of genetic algorithms. The performance of the developed hybrid pavement auscultation technique is evaluated through extensive field studies conducted at a state-of-the-art full-scale airfield pavement test facility. The results show that this approach is promising for real-time condition evaluation of airfield pavement infrastructure systems. [ABSTRACT FROM PUBLISHER]

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background. [ABSTRACT FROM AUTHOR]

Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro–fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cluster synchronization problem for linearly coupled networks, which can be recurrently connected neural networks, cellular neural networks, Hodgkin–Huxley models, Lorenz chaotic oscillators, etc., by adding some simple intermittent pinning controls. We assume the nodes in the network to be identical and the coupling matrix to be asymmetric. Some sufficient conditions to guarantee global cluster synchronization are presented. Furthermore, a centralized adaptive intermittent control is introduced and theoretical analysis is provided. Then, by applying the adaptive approach on the diagonal submatrices of the asymmetric coupling matrix, we also get the corresponding cluster synchronization result. Finally, numerical simulations are given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel steam valving controller using radial basis function (RBF) networks-based approximate model method. Approximate model method is a kind of direct linearization approach that is derived based on the approximation of the plant's input-output model via Taylor expansion. RBF networks are used to identify the plant to implement the approximate model control law. In order to improve the performance of the approximate model controller, RBF networks weights are adjusted online using BP algorithms with an adaptive learning rate. Several simulations results demonstrate the effectiveness of the proposed controller for team valving control. [ABSTRACT FROM AUTHOR]

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models-in terms of explanatory variables and NN structures-we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models. [ABSTRACT FROM AUTHOR]

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on κ-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. κ-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity κ and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of κ. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications. [ABSTRACT FROM AUTHOR]

Optimizing the traffic signal control has an essential impact on intersections efficiency in urban transportation. This paper presents a two-stage method for intersection signal timing control. First, the traffic volume is predicted using a neuro-fuzzy network called Adaptive neuro-fuzzy inference system (ANFIS). The inputs of this network include two-dimensional, hourly and daily, traffic volume correlations. In the second stage, appropriate signal cycle and optimized timing of each phase of the signal are estimated using a combination of Self Organizing and Hopfield neural networks. The energy function of the Hopfield network is based on a traffic model derived by queuing analysis. The performance of the proposed method has been evaluated for real data. The two-dimensional correlation presents superior performance compared to hourly traffic correlation. The evaluation of proposed overall method shows considerable intersection throughput improvement comparing to the results taken form Synchro software. [ABSTRACT FROM AUTHOR]

Multiscale neurofuzzy modeling combines the multiresolution property of the wavelet transform with the regression ability of neurofuzzy systems. A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in detail and approximation. A neurofuzzy system is then trained on each of the relevant resolution scales (i.e. those scales where significant events are detected); and individual wavelet forecasts are recombined to form the overall forecast. The neurofuzzy models developed in this paper are based on Mamdani and Takagi–Sugeno–Kang approaches to the problem of fuzzy modeling based on the strategy knowledge expressed by the input-output data. Within these approaches, the proposed Neural-Fuzzy Inference System (NFIS) provides several methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with the learning power of neural networks. Simulation results carried out on a forecasting problem associated with stock market, are included to demonstrate the potential of the proposed forecasting scheme. [ABSTRACT FROM AUTHOR]

Based on the basic formulation developed in a companion paper, the writers now present the application of an artificial neural network approach to designing streamlined network models to simulate and identify the nonlinear dynamic response of single-degree-of-freedom oscillators using the restoring force-state mapping interpretation. The neural networks which use sigmoidal activation functions are shown to be highly robust in modeling a wide variety of commonly observed nonlinear structural dynamic response behaviors. By streamlining the networks, individual network model parameters take on physically or geometrically interpretable meaning, and hence, the network initialization can be achieved through an engineered approach rather than through less physically meaningful numerical initialization schemes. Although not proven in general, examples show that by starting with a more meaningful initial design, identification convergence is improved, and the final identified model parameters are seen to have a more physical meaning. A set of model architecture prototypes is developed to capture commonly observed nonlinear response behaviors. [ABSTRACT FROM AUTHOR]

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feed forward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed. [ABSTRACT FROM AUTHOR]

The neuro-fuzzy refers to the recent technology that couples the traditional fuzzy logic developments with neural nets training capabilities to compose the fuzzy logic’s knowledge base and fuzzy sets’ parameters optimally. This paper discusses the calibration methodology of a neuro-fuzzy logic for modeling the route choice behavior. The logic accounts for the various factors of potential effect on the route choice utility perceived by the traveler. The structure of the fuzzy control stages, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model. The results of the fuzzy logic model are utilized for in-depth analyses of the travelers’ perceptions of the route utility in response to the various traffic states. [ABSTRACT FROM AUTHOR]

The extension of estimation of distribution algorithms (EDAs) to the multi-objective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding 'standard' multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the model-building issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements. [ABSTRACT FROM AUTHOR]

In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches $64.38$ percent by our ATR framework, significantly higher than $44.76$ percent based on the state-of-the-art algorithm [28]. [ABSTRACT FROM PUBLISHER]

An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters. [ABSTRACT FROM AUTHOR]

We present coupled spin torque nano oscillators (STNOs) as electronic neurons for efficient brain-inspired computation. The coupled STNOs show two distinct outputs, depending on whether the frequencies are locked or not. The locking mechanisms are based on magnetic coupling or injection locking. The neuron firing threshold can be set by tuning the locking range of the coupled STNOs. We employ a crossbar array of programmable memory devices like memristors to implement electronic synapses that work seamlessly with the coupled STNOs for hardware implementation of neural networks. Results show that injection locking-based neuron model can be attractive from scaling point of view and computation like character recognition can be performed with energy consumption per neuron of $\sim $ 1.8X and $\sim $ 3X lower than the digital and the analog CMOS counterpart, respectively. [ABSTRACT FROM AUTHOR]

In this paper, neural algorithms, including the multi-layered perceptron (MLP) differential approximator, generalized hybrid power series, discrete Hopfield neural network, and the hybrid numerical, are used for constructing models that incorporate a priori knowledge in the form of differential equations for dynamic engineering processes. The properties of these approaches are discussed and compared to each other in terms of efficiency and accuracy. The presented algorithms have a number of advantages over other traditional mesh-based methods such as reduction of the computational cost, speed up of the execution time, and data integration with the a priori knowledge. Furthermore, the presented techniques are applicable when the differential equations governing a system or dynamic engineering process are not fully understood. The proposed algorithms learn to compute the unknown or free parameters of the equation from observations of the process behavior, hence a more precise theoretical description of the process is obtained. Additionally, there will be no need to solve the differential equation each time the free parameters change. The parallel nature of the approaches outlined in this paper make them attractive for parallel implementation in dynamic engineering processes. [ABSTRACT FROM AUTHOR]