Search

Abstract: This paper presents a new approach to analyze or to select the most influential inputs for locating the reason for poor education among rural people, with the use of bidirectional associative memories (BAM). Quantifying the reason for poor education in rural areas can improve the education of rural people. This paper develops a extended BAM (EBAM) , using BAM and it tries to test this method for analyzing the reason for poor eduction in the rural areas. This analysis can be used to determine the method of education for rural people, and it can help to improve the quality or level of education of rural people. So, this paper uses the EBAM with different neurons for the problem of poor education in rural area. [Copyright &y& Elsevier]

Image/Video Quality Assessment (IQA/VQA) plays a significant role in image and video processing, as it can directly predict the impact of distortions on the video in the quality of experience (QoE) of the user. For this propose, in this paper, it is presented a new method for objective video quality assessment using an artificial neural network to predict the subjective evaluation of the video as if it were observed by a human user. The network was trained using degradation indicators extracted from the VQEG Phase I video database, which describe the level of distortion suffered by the original video under spatial and temporal scopes. The proposed method obtained an excellent correlation with the subjective scores over this same database. [ABSTRACT FROM AUTHOR]

Abstract: Neural networks have shown good results for detecting a certain pattern in a given image. In this paper, faster neural networks for pattern detection are presented. Such processors are designed based on cross correlation in the frequency domain between the input matrix and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the detection process. The principle of divide and conquer strategy is applied through matrix decomposition. Each matrix is divided into smaller in size submatrices and then each one is tested separately by using a single faster neural processor. Furthermore, faster pattern detection is obtained by using parallel processing techniques to test the resulting submatrices at the same time using the same number of faster neural networks. In contrast to faster neural networks, the speed up ratio is increased with the size of the input matrix when using faster neural networks and matrix decomposition. Moreover, the problem of local submatrix normalization in the frequency domain is solved. The effect of matrix normalization on the speed up ratio of pattern detection is discussed. Simulation results show that local submatrix normalization through weight normalization is faster than submatrix normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line. [Copyright &y& Elsevier]

Abstract: Electronic negotiation experiments provide a rich source of information about relationships between the negotiators, their individual actions, and the negotiation dynamics. This information can be effectively utilized by intelligent agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics. This paper presents an approach to modeling the negotiation process in a time-series fashion using artificial neural network. In essence, the network uses information about past offers and the current proposed offer to simulate expected counter-offers. On the basis of the model’s prediction, “what-if” analysis of counter-offers can be done with the purpose of optimizing the current offer. The neural network has been trained using the Levenberg–Marquardt algorithm with Bayesian Regularization. The simulation of the predictive model on a testing set has very good and highly significant performance. The findings suggest that machine learning techniques may find useful applications in the context of electronic negotiations. These techniques can be effectively incorporated in an intelligent agent that can sense the environment and assist negotiators by providing predictive information, and possibly automating some negotiation steps. [Copyright &y& Elsevier]

Abstract: The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. An early detection of faults may help to avoid product deterioration, performance degradation, major damage to the machinery itself and damage to human health or even loss of lives. The centrifugal pumping rotary system is considered for this research. This paper presents the development of artificial neural network-based model for the fault detection of centrifugal pumping system. The fault detection model is developed by using two different artificial neural network approaches, namely feed forward network with back propagation algorithm and binary adaptive resonance network (ART1). The training and testing data required are developed for the neural network model that were generated at different operating conditions, including fault condition of the system by real-time simulation through experimental model. The performance of the developed back propagation and ART1 model were tested for a total of seven categories of faults in the centrifugal pumping system. The results are compared and the conclusions are presented. [Copyright &y& Elsevier]

Abstract: Almost all current training algorithms for neural networks are based on gradient descending technique, which causes long training time. In this paper, we propose a novel fast training algorithm called Fast Constructive-Covering Algorithm (FCCA) for neural network construction based on geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. By doing this, FCCA is able to avoid iterative computing and much faster than traditional training algorithms. Given an input sequence in an arbitrary order, FCCA learns “easy” samples first and “confusing” samples are easily learned after these “easy” samples. This sample reordering process is done on the fly based on geometrical concept. In addition, FCCA begins with an empty hidden layer, and adds new hidden neurons when necessary. This constructive learning avoids blind selection of neural network structure. The experimental work for classification problems illustrates the advantages of FCCA, especially in learning speed. [Copyright &y& Elsevier]

Abstract: We describe in this paper the application of a modular neural network architecture to the problem of simulating and predicting the dynamic behavior of complex economic time series. We use several neural network models and training algorithms to compare the results and decide at the end, which one is best for this application. We also compare the simulation results with the traditional approach of using a statistical model. In this case, we use real time series of prices of consumer goods to test our models. Real prices of tomato in the U.S. show complex fluctuations in time and are very complicated to predict with traditional statistical approaches. For this reason, we have chosen a neural network approach to simulate and predict the evolution of these prices in the U.S. market. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to the analysis of asymptotic stability of artificial neural networks (ANN) with multiple time-varying delays subject to polytope-bounded uncertainties. This approach is based on the Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique with the use of a recent Leibniz–Newton model based transformation without including any additional dynamics. Three examples with numerical simulations are used to illustrate the effectiveness of the proposed method. The first example considers the neural network with multiple time-varying delays, which may be seen as a particular case of the second example where it is subject to uncertainties and multiple time-varying delays. Finally, the third example analyzes the stability of the neural network with higher numbers of neurons subject to a single time-delay. The Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability in the bifurcation point. [Copyright &y& Elsevier]

Abstract: Importance of early prediction of bad creditors has been increasing extensively. In this paper, we propose a behavioral scoring model which dynamically accommodates the changes of borrowers’ characteristics after the loans are made. To increase the prediction efficiency, the data set is segmented into several clusters and the observation period is fractionized. The computational results showed that the proposed model can replace the currently used static model to minimize the loss due to bad creditors. The results of this study will help the loan lenders to protect themselves from the potential borrowers with high default risks in a timely manner. [Copyright &y& Elsevier]

Abstract: The construction of computational models with provision for effective learning and added reasoning is a fundamental problem in computer science. In this paper, we present a new computational model for integrated reasoning and learning that combines intuitionistic reasoning and neural networks. We use ensembles of neural networks to represent intuitionistic theories, and show that for each intuitionistic theory and intuitionistic modal theory there exists a corresponding neural network ensemble that computes a fixed-point semantics of the theory. This provides a massively parallel model for intuitionistic reasoning. In our model, the neural networks can be trained from examples to adapt to new situations using standard neural learning algorithms, thus providing a unifying foundation for intuitionistic reasoning, knowledge representation, and learning. [Copyright &y& Elsevier]

Abstract: The paper deals with the identification of recurrent neural networks (RNNs) for simulating the air–fuel ratio (AFR) dynamics into the intake manifold of a spark ignition (SI) engine. RNN are derived from the well-established static multi layer perceptron feedforward neural networks (MLPFF), that have been largely adopted for steady-state mapping of SI engines. The main contribution of this work is the development of a procedure that allows identifying a RNN-based AFR simulator with high generalization and limited training data set. The procedure has been tested by comparing RNN simulations with AFR transients generated using a nonlinear-dynamic engine model. The results show how training the network making use of inputs that are uncorrelated and distributed over the entire engine operating domain allows improving model generalization and reducing the experimental burden. Potential areas of application of the procedure developed can be either the use of RNN as virtual AFR sensors (e.g. engine or individual AFR prediction) or the implementation of RNN in the framework of model-based control architectures. [Copyright &y& Elsevier]

Abstract: This paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results. [Copyright &y& Elsevier]

Abstract: In this paper, the Cohen–Grossberg neural networks (CGNNs) with variable and unbounded delays are studied. By using the Lipschitzian Hadamard Theorem and a property of homeomorphism mapping, some new sufficient conditions are obtained to ensure the existence, uniqueness and stability of the equilibrium point. The activation functions need only to be partially Lipschitz continuous and monotone nondecreasing, but do not require to be bounded or differentiable. [Copyright &y& Elsevier]

Abstract: In recent years, neural networks are widely being used in areas where conventional statistical methods were used. This paper compares regression and neural networks on a real life data and two simulated examples. The results show that regression is much better than neural networks for skewed data. General guidelines are suggested to improve the performance of neural networks for skewed data. [Copyright &y& Elsevier]

Abstract: The proposed adaptable control method for linearization of high power amplifiers is powered by the neural networks technique that supports analogue polynomial type of predistorters, which are widely utilized in commercial power amplifiers for wireless communication purposes. This paper presents an algorithm to determine the coefficients by use of artificial neural networks and its generalization feature that helps to map the power amplifier response with optimal coefficients of the polynomial, which in a proper way pre-distorts an input signal of the amplifier. The concept of the predistortion has been introduced. Furthermore, the overall step-by-step initialization and functionality of the control process has also been described. The method has been tested successfully in a real power amplifier equipped with an analogue predistorting circuits. Presented measurement results imply that this approach is robust and well suited for such category of the power amplifier design, in which the artificial neural networks play substantial role. [Copyright &y& Elsevier]

Abstract: In this paper, a general class of recurrent neural networks with time-varying delays is studied. Some novel and sufficient conditions are given to guarantee the global exponential stability of the equilibrium point and the existence of periodic solutions for such delayed neural networks. Comparing with some previous literature, in which the time-varying delays were assumed to be differentiable and their derivatives were simultaneously required to be not greater than 1, the restrictions on the time-varying delays are removed. Therefore, our results obtained here improve and extend some previously related results. Finally, two numerical examples are provided to illustrate our theorems. [Copyright &y& Elsevier]

The impact of weaving vehicles on the capacity of freeway segments is uncertain due to the complexity in operation. The Highway Capacity Manual (HCM) 2000 provides values for capacity on various weaving segments (Exhibit 24-8) based on sets of conditions (configuration, speed, length, volume ratio, and number of lanes). However, to find capacity for a given set of conditions, an iterative process should be carried out using a properly programmed spreadsheet. This paper suggests alternative and convenient procedure for estimating capacity on weaving segments. Two capacity prediction models are developed using regression and neural networks (NNT). Although, linear regression (LR) technique showed satisfactory results, neural network technique outscored linear regression in the prediction performance, and generalization ability. The trained neural network architecture represented by weight and bias values for each layer is simply used to predict capacity for weaving segments under new conditions. [Copyright &y& Elsevier]

This paper is concerned with a delay differential system with piecewise constant nonlinearity. Such a differential system can be regarded as a model of two-neuron artificial neural network with delayed feedback. Some interesting results are obtained for the convergence and periodicity of solutions of the system. [Copyright &y& Elsevier]

Effective sensorless dynamic speed estimation is desirable for both speed sensorless motor-drive applications and for on-line induction motor condition monitoring and assessment. In this paper, a sensorless neural adaptive speed filter is developed for induction motors running off a voltage source inverter. The filter is demonstrated by comparisons with experimental speed measurements and spectral speed estimates. In addition to nameplate information required for the initial set-up, the proposed neural speed filter uses only measured motor currents and voltages. Initial training of the speed filter is accomplished off-line, using rotor slot harmonic-based speed estimates. The developed speed filter is tested on data from a healthy motor and motor with 4 broken rotor bars. The resulting speed filtering accuracy compares favorably with speed estimation results reported in the literature. The study demonstrates the feasibility of neural adaptive speed filters for effective inverter-fed induction motor speed estimation, without explicit use of motor model parameters and speed measurements. [Copyright &y& Elsevier]