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In this paper, a recently developed fiber tracking algorithm to be used with diffusion tensor (DT) fields acquired via magnetic resonance imaging (MRI) is improved and applied to real brain DT-MR images. The method performs satisfactorily in regions where branching and crossing fibers exist and offers the capability of reporting a probability value for the computed tracts. This certainty figure takes into account both the anisotropy and the information provided by all the eigenvectors and eigenvalues of the diffusion matrix at each voxel. In previous papers of the authors, a simpler algorithm was applied only to elementary synthetic DT-MR images. As now presented, this algorithm is now adequately used with more intricate synthetic images and is applied to real white matter DT-MR images with successful results. A novel neural network is used to estimate the main parameters of the algorithm. Numerical experiments show a performance gain over previous approaches, specially with respect to convergence and computational load. The tracking of white matter fibers in the human brain will improve the diagnosis and treatment of many neuronal diseases. [ABSTRACT FROM AUTHOR]

This paper presents a new neuro-based method for short term load forecasting of Iran national power system (INPS). A MultiLayer Perceptron (MLP) based Neural Network (NN) toolbox has been develeped to forecast 168 hours ahead. The proposed MLP has one hiden layer with 5 neurons. The effective inputs were selected through a peer investigation on historical data released from the INPS. To adjust the parameters of the MLP, the Levenberg-Marquardt Back Propagation (LMBP) training algorithm has been employed because of its remarkable fast speed of convergence. Most of papers dealt with 168-hour forecasting employed a hirachical method in the sense of monthly or seasonly provided that there are enough data. In the absence of rich data, forecasting error would increase. To remedy this problem, the proposed neuro-based approach uses only the weekly group data of concern while an extra input is added up to indicate the month. In other words for each weekly group, a unique MLP based neural network is designed for the purposed of load forecasting. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using the minimum Euclidean distance. Simulation results are provided and show that the CVQ system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. [ABSTRACT FROM AUTHOR]

A new approach to approximate the algebraic solution of systems of interval linear equations (SILE) is proposed in this paper. The original SILE problem is first transformed into an optimization problem, which is in turn solved with use of artificial neural networks and gradient-based optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper we introduce technique how a neural network can generate a Hidden Markov Chain. We use neural network called Temporal Information Categorizing and Learning Map. The network is an enhanced version of standard Categorizing and Learning Module (CALM). Our modifications include Euclidean metrics instead of weighted sum formerly used for categorization of the input space. Construction of the Hidden Markov Chain is provided by turning steady weight internal synapses to associative learning synapses. Result obtained from testing on simple artificial data promises applicability in a real problem domain. We present a visualization technique of the obtained Hidden Markov Chain and the method how the results can be validated. Experiments are being performed. [ABSTRACT FROM AUTHOR]

In this paper we present an architecture for decision making of software agents that allows the agent to be-have autonomously. Our target area is computational agents — encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is based on the vertically-layered and belief-desire-intention architectures. Several experiments with computational agents were conducted to demonstrate the benefits of the architecture. [ABSTRACT FROM AUTHOR]

This paper describes the application of Hybrid Intelligent Systems in a new domain: reliability of complex networks. The reliability is assessed by employing two algorithms (TREPAN and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)), both belonging to the Hybrid Intelligent Systems paradigm. TREPAN is a technique to extract linguistic rules from a trained Neural Network, whereas ANFIS is a method that combines fuzzy inference systems and neural networks. In the experiment presented, the structure function of the complex network analyzed is properly emulated by training both models on a subset of possible system configurations, generated by a Monte Carlo simulation and an appropriate Evaluation Function. Both approaches are able to successfully describe the network status through a set of rules, which allows the reliability assessment [ABSTRACT FROM AUTHOR]

Thinking about the “Symbol Grounding Problem” and the brain structure of living things, the author believes that it is the best solution for generating communication in robot-like systems to use a neural network that is trained based on reinforcement learning. As the first step of the research of symbol emergence using neural network, it was examined that parallel analog communication signals are binarized in some degree by noise addition in reinforcement learning-based communication acquisition. In this paper, it is shown that two consecutive analog communication signals are binarized by noise addition using recurrent neural networks. Furthermore, when the noise ratio becomes larger, the degree of the binarization becomes larger. [ABSTRACT FROM AUTHOR]

Artificial Neural networks (ANNs) have found applications in a wide variety of medical problems and have proved successful for non-linear regression and classification. This paper details a novel and flexible probabilistic non-linear ANN model for the prediction of conditional survival probability of malignant melanoma patients. Hazard and probability density functions are also estimated. The model is trained using the log-likelihood function, and generalisation has been addressed. Unrestricted by assumptions that are unrealistic or parametric forms that are difficult to justify, the model thereby attains advantage over traditional statistical models. Furthermore, an estimate of the variance-covariance matrix is obtained using the asymptotic Fisher information matrix. Implemented in an Excel® spreadsheet, the model’s user-friendly design further adds to its flexibility, with much potential for use by statisticians as well as researchers. [ABSTRACT FROM AUTHOR]

In this paper, we present a neural network based system to generate an adaptive model for financial time series tracking. This kind of data is quite relevant for data quality monitoring in large databases. The proposed system uses the past samples of the series to indicate its future trend and to generate a corridor inside which the future samples should lie. This corridor is derived from an adaptive forecasting model, which makes use of the walk-forward method to take into account the most recent observations of the series and bring up to date the values of the neural model parameters. The model can serve also to manage other time series characteristics, such as the detection of irregularities. [ABSTRACT FROM AUTHOR]

Using a GA as a NN designing tool deals with many aspects. We must decide, among others, about: coding schema, evaluation function, genetic operators, genetic parameters, etc. This paper focuses on an efficiency of NN architecture evolution. We use two main approaches for neural network representation in the form of chromosomes: direct and indirect encoding. Presented research is a part of our wider study of this problem [1, 2]. We present the influence of coding schemata on the possibilities of evolving optimal neural network. [ABSTRACT FROM AUTHOR]

Temporal gene expression data is of particular interest to researchers as it can be used to create regulatory gene networks. Such gene networks represent the regulatory relationships between genes over time and provide insight into how genes up- and down-regulate each other from one time-point to the next (the Biological Motherboard). Reverse engineering gene networks from temporal gene expression data is considered an important step in the study of complex biological systems. This paper introduces sensitivity analysis of trained perceptions to reverse engineer the gene networks from temporal gene expression data. It is shown that a trained neural network, with pruning (gene silencing), can also be described as a gene network with minimal re-interpretation, where the sensitivity between nodes reflects the probability of one gene affecting another gene in time. The methodology is known as the Neural Network System Biology Approach with Gene Silencing Simulations (NNSBAGSS). The methodology was applied to artificial temporal data and rat CNS development time-course data. [ABSTRACT FROM AUTHOR]

The assessment and forecast for atmospheric quality have become the key problem in the study of the quality of atmospheric environment. In order to evaluate the grade of the atmospheric pollution, a model based on the particle swarm optimization (PSO) algorithm is proposed in this paper. Experimental results show the advantages of the proposed models, such as pellucid principle and physical explication, predigested formula and low computation complexity. In addition, an improved Elman neural network, namely, the output-input feedback Elman (OIF Elman) neural network is also applied to forecast the atmospheric quality. Simulations show that the OIF Elman neural network has great potential in the field of forecasting the atmospheric quality. [ABSTRACT FROM AUTHOR]

An evolutionary algorithm based on Random Keys to represent Golomb Rulers segments, has been found to be a reliable option for finding Optimal Golomb Rulers in a short amount of time, when comparing with standard methods. This paper presents a modified version of this evolutionary algorithm where the maximum segment length for a Golomb Ruler is also part of the evolutionary process. Attained experimental results shows us that this alteration does not seems to provide significant benefits to the static version of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the evolution of blackjack strategies, that of cultural learning. Populations of neural network agents are evolved using a genetic algorithm and at each generation the best performing agents are selected as teachers. Cultural learning is implemented through a hidden layer in each teacher’s neural network that is used to produce utterances which are imitated by its pupils during many games of blackjack. Results show that the cultural learning approach outperforms previous work and equals the best known non-card counting human approaches. [ABSTRACT FROM AUTHOR]

In the context of evolutionary data classification, dynamical modeling techniques are useful to continuously learn clusters models. Dedicated to on-line clustering, the AUDyC (Auto-adaptive and Dynamical Clustering) algorithm is an unsupervised neural network with auto-adaptive abilities in nonstationary environment. These particular abilities are based on specific learning rules that are developed into three stages: “Classification”, “Evaluation” and “Fusion”. In this paper, we propose a new densities merge mechanism to improve the “Fusion” stage in order to avoid some local optima drawbacks of Gaussian fitting. The novelty of our approach is to use an ambiguity rule of fuzzy modelling with new merge acceptance criteria. Our approach can be generalized to any type of fuzzy classification method using Gaussian models. Some experiments are presented to show the efficiency of our approach to circumvent to AUDyC NN local optima problems. [ABSTRACT FROM AUTHOR]

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, one of the limitations of Self Organizing Maps algorithm, is that every SOM is different and finds different similarities among the sample vectors each time the initial conditions are changed. In this paper, we propose a modification of the SOM basic algorithm in order to make the resulted mapping invariant to the initial conditions. We extend the neighborhood concept to processing units, selected in a fashionable manner, other than those commonly selected relatively to the immediate surroundings of the best matching unit. We also introduce a new learning function for the newly introduced neighbors. The modified algorithm was tested on a color classification application and performed very well in comparison with the traditional SOM. [ABSTRACT FROM AUTHOR]

In this paper, a new clustering algorithm based on CLA-EC is proposed. The CLA-EC is a model obtained by combining the concepts of cellular learning automata and evolutionary algorithms. The CLA-EC is used to search for cluster centers in such a way that minimizes the squared-error criterion. The simulation results indicate that the proposed algorithm produces clusters with acceptable quality with respect to squared-error criteria and provides a performance that is significantly superior to that of the K-means algorithm. [ABSTRACT FROM AUTHOR]

The paper introduces a new type of ontogenic neural networks called Interval Basis Neural Networks (IBNNs). The IBNN configures the whole topology and computes all weights after a priori knowledge collected form training data. The training patterns are grouped together producing intervals separately for all input features for each class after statistical analyses of the training data. This IBNNs feature make possible to computed all network parameters without training. Moreover, the IBNN takes into account the distances between patterns of the same classes and builds the well-approximating model especially on the borders between the classes. Furthermore, the IBNNs are insensitive for quantity differences in patterns representation of classes. The IBNNs always correctly classify training data and very good generalize other data. [ABSTRACT FROM AUTHOR]

The local cluster neural network (LCNN) is an alternative to RBF networks that performs well in digital simulation. The LCNN is suitable for an analog VLSI implementation that is attractive for a wide range of embedded neural net applications. In this paper, we present the input-output characterisation of LCNN analog chip. The effect of manufacturing variations on the chip’s function is investigated and analyzed. [ABSTRACT FROM AUTHOR]

This paper presents the elaboration and validation of “soft sensor” using neural networks for on-line estimation of the coagulation dose from raw water characteristics. The main parameters influencing the coagulant dosage are firstly determined via a PCA. A brief description of the methodology used for the synthesis of neural model is given and experimental results are included. The training of the neural network is performed using the Weight Decay regularization in combination with Levenberg-Marquardt method. The performance of this soft sensor is illustrated with real data. [ABSTRACT FROM AUTHOR]

This paper proposes the use of Artificial Neural Networks (ANN) for the prediction of the maximum surface crack width of precast reinforced concrete beams joined by steel coupler connectors and anchor bars (jointed beams). Two different training algorithms are used in this study and their performances are compared. The first approach used Back propagation (BPANN) and the second one includes Genetic Algorithms (GANN) during the training process. Input and output vectors are designed on the basis of empirical equations available in the literature to estimate crack widths in common reinforced concrete (RC) structures and parameters that characterize the mechanical behavior of RC beams with overlapped reinforcement. Two well-defined points of loading are considered in this study to demonstrate the suitability of this approach in both, a linear and a highly nonlinear stage of the mechanical response of this type of structures. Remarkable results were obtained, however, in all cases the combined Genetic Artificial Neural Network approach resulted in improved prediction performance over networks trained by error back propagation. [ABSTRACT FROM AUTHOR]

In this paper, we study the difference between two ways of setting synaptic weights in a “temporal” neural network. Used as a controller of a simulated mobile robot, the neural network is alternatively evolved through an evolutionary algorithm or trained via an hebbian reinforcement learning rule. We compare both approaches and argue that in the last instance only the learning paradigm is able to exploit meaningfully the temporal features of the neural network. [ABSTRACT FROM AUTHOR]

This paper proposes a general method that transforms known neural network MSA algorithms, into MCA algorithms. The method uses two distinct time scales. A given MSA algorithm is responsible, on a faster time scale, for the “behavior” of all output neurons. On this scale minor subspace is obtained. On a slower time scale, output neurons compete to fulfill their “own interests”. On this scale, basis vectors in the minor subspace are rotated toward the minor eigenvectors. Actually, time-oriented hierarchical method is proposed. Some simplified mathematical analysis, as well as simulation results are presented. [ABSTRACT FROM AUTHOR]

Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators. In this paper we present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D function approximation. Our purpose is to approximate an unknown function f: Rn → R from scattered samples (xi; yi = f(x)) i=1.…n, where: we have little a priori knowledge on the unknown function f which lives in some infinite dimensional smooth function space,the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate $${\hat f}$$ as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network. [ABSTRACT FROM AUTHOR]

Our recent works on artificial neural networks point to the possibility of extending the activation function of a standard artificial neuron model using the conditional signal accumulation technique, thus significantly enhancing the capabilities of neural networks. We present a new artificial neuron model, called Sigma-if, with the ability to dynamically tune the size of the decision space under consideration, resulting from a novel activation function. The paper discusses construction of the proposed neuron as well as training Sigma-if feedforward neural networks for well known sample classification problems. [ABSTRACT FROM AUTHOR]

When feedforward neural networks of multi-layer perceptron (MLP) type are used as black-box models of complex processes, a common problem is how to select relevant inputs from a large set of potential variables that affect the outputs to be modeled. If, furthermore, the observations of the input-output tuples are scarce, the degrees of freedom may not allow for the use of a fully connected layer between the inputs and the hidden nodes. This paper presents a systematic method for selection of both input variables and a constrained connectivity of the lower-layer weights in MLPs. The method, which can also be used as a means to provide initial guesses for the weights prior to the final training phase of the MLPs, is illustrated on a class of test problems. [ABSTRACT FROM AUTHOR]