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检索条件"主题词=Backpropagation Algorithms"

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Recognition of unconstrained handwritten digits using modified chaotic neural networks

Recognition of unconstrained handwritten digits using modifi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Han-Go Choi Jae-Heung Cho Sang-Hee Kim Sang-Jae Lee School of Electronic Engineering Kum-Oh National University of Technology South Korea

This paper describes an off-line method for recognizing totally unconstrained handwritten digits using modified chaotic neural networks (CNN). Since the CNN has inherently the characteristics of highly nonlinear dynamics it can be an appropriate network for the robust classification of complex patterns. The CNN in this paper is trained by the error backpropagation algorithm. Digit identification starts with extraction of features from the raw digit images and then recognizes digits using the CNN based classifier The performance of the CNN classifier is evaluated on the Concordia database. For the relative comparison of recognition performance the CNN classifier is compared with the recurrent neural networks (RNN) classifier Experimental results show that the classification rate is 98.4%. It indicates that the CNN classifier outperforms the RNN classifier as well as other classifiers that have been reported on the same database.

关键词： Handwriting recognition Cellular neural networks Recurrent neural networks Chaos Neural networks Robustness backpropagation algorithms Feature extraction Image recognition Image databases

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Preconditioning method to accelerate neural networks gradient training algorithms

Preconditioning method to accelerate neural networks gradien...

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International Joint Conference on Neural Networks (IJCNN)

作者： M.J. Perez-Ilzarbe Dpto de Automática y Computación Universidad PÙblica de Navarra Pamplona Spain

In this work a simple method for conditioning neural networks gradient training algorithms is presented. It consists of using a different learning rate for the outgoing weights of each one of the neurons or network input nodes. In the case of one layer neural networks the method can also be implemented by normalizing the input training examples in certain way. The performance of the method proposed has been tested in the training of neural networks to solve a problem of image recognition. A considerable acceleration of the training algorithms has been attained in the examples tested.

关键词： Acceleration Neural networks Neurons Multi-layer neural network backpropagation algorithms Vectors Eigenvalues and eigenfunctions Testing Image recognition Convergence

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High bandwidth direct adaptive neurocontrol of induction motor current and speed using continual online random weight change training

High bandwidth direct adaptive neurocontrol of induction mot...

引用

Annual IEEE Conference on Power Electronics Specialists (PESC)

作者： B. Burton R.G. Harley T.G. Habetler Department of Electrical Engineering University of Natal Durban South Africa Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

This paper reports on direct adaptive neurocontrol of induction motors using the random weight change (RWC) training algorithm for continually online trained (COT) neural network (NN) ASICs. A previous practical implementation of a direct backpropagation (BP) COT NN induction motor (IM) current control scheme is used to demonstrate the sufficiency of local minimum tracking (LMT) for direct control. Subsequent work on the substitution of RWC-COT into this current control scheme is also summarised. New work is presented which shows that, while sufficient for direct control, LMT can cause instability in NN model based indirect control schemes. This new work also shows how the BP-COT NN controller of an indirect IM speed control scheme may be replaced by a RWC-COT NN in a direct control structure to effectively avoid LMT instability.

关键词： Bandwidth Induction motors Neural networks Current control Velocity control Power electronics Control systems Power system modeling Adaptive control backpropagation algorithms

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Adaptive critic neural network for feedforward compensation

Adaptive critic neural network for feedforward compensation

引用

American Control Conference (ACC)

作者： J. Campos F.L. Lewis Automation and Robotics Research Institute University of Technology Fort Worth TX USA

The paper is concerned with the application of adaptive critic techniques to feedback control of nonlinear systems using neural networks (NN). No initial model for the nonlinear system is necessary. The work shows how to cope with nonlinearities through adaptive critics with no preliminary off-line learning required. The proposed scheme consists of a feedforward action generating NN that compensates for the unknown system nonlinearities. The learning of this NN is performed online based on a signal from a second neural network acting as a critic. The critic NN tunes itself online using the performance measure of the system. Both NN tuning algorithms are based on backpropagation, which must be modified to guarantee closed-loop performance. The two algorithms are derived using nonlinear stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system.

关键词： Adaptive systems Neural networks Feedforward neural networks Nonlinear systems backpropagation algorithms Stability analysis Programmable control Adaptive control Feedback control Convergence

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The Hausdorff distance measure for feature selection in learning applications

The Hausdorff distance measure for feature selection in lear...

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Annual Hawaii International Conference on System Sciences (HICSS)

作者： S. Piramuthu Operations and Information Management The Wharton School University of Pennsylvania Philadelphia PA USA

Recent advances in computing technology in terms of speed, cost, as well as access to tremendous amounts of computing power and the ability to process huge amounts of data in reasonable time has spurred increased interest in data mining applications. Machine learning has been one of the methods used in most of these data mining applications. It is widely acknowledged that about 80% of the resources in a majority of data mining applications are spent on cleaning and pre-processing the data. However, there have been relatively few studies on pre-processing data used as input in these data mining systems. In this study, we present a feature selection method based on the Hausdorff distance measure, and evaluate its effectiveness in pre-processing input data for inducing decision trees. The Hausdorff distance measure has been used extensively in computer vision and graphics applications to determine the similarity of patterns. Two real-world financial credit scoring data sets are used to illustrate the performance of the proposed method.

关键词： Genetic algorithms Mathematical programming Statistical analysis Analysis of variance Noise generators backpropagation algorithms Image converters Encoding Neural networks Infrared imaging

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Acceleration of learning in feedforward networks using dynamical systems analysis and matrix perturbation theory

Acceleration of learning in feedforward networks using dynam...

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International Joint Conference on Neural Networks (IJCNN)

作者： N. Ampazis S.J. Perantonis J.G. Taylor National Center for Scientific Research DEMOKRITOS Institute of Informatics and Telecommunications Athens Greece Department of Mathematics King''s College London UK

For the explanation of the dynamical behavior of learning in feedforward networks, the recent work by the authors (1999) has focused on the derivation of a dynamical system model which is valid in the vicinity of temporary minima caused by redundancy of nodes in the hidden layer. The purpose of this paper is to show how to incorporate information from the dynamical system model into a constrained optimization algorithm that will allow prompt abandonment of temporary minima and therefore facilitate learning in feedforward network. It is shown that such a formalism can be obtained by the application of matrix perturbation theory. Experimental results illustrate the analytical conclusions.

关键词： Acceleration Intelligent networks Eigenvalues and eigenfunctions Bifurcation Neurons Jacobian matrices Cost function Constraint optimization backpropagation algorithms Difference equations

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Efficient algorithm for training neural networks with one hidden layer

Efficient algorithm for training neural networks with one hi...

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International Joint Conference on Neural Networks (IJCNN)

作者： B.M. Wilamowski Yixin Chen A. Malinowski EE Department University of Wurzburg Laramie WY USA ECET Department Bradley University Peoria IL USA

Efficient second order algorithm for training feedforward neural networks is presented. The algorithm has a similar convergence rate as the Lavenberg-Marquardt (LM) method and it is less computationally intensive and requires less memory. This is especially important for large neural networks where the LM algorithm becomes impractical. Algorithm was verified with several examples.

关键词： Neural networks Jacobian matrices backpropagation algorithms Equations Convergence Feedforward neural networks Stochastic processes Neurons Stability Performance analysis

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Comparative analysis of artificial neural network models: application in bankruptcy prediction

Comparative analysis of artificial neural network models: ap...

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International Joint Conference on Neural Networks (IJCNN)

作者： C. Charalambous A. Charitou F. Kaourou Department of Business Administration University of Cyprus Nicosia Cyprus

This study compares the predictive performance of three neural network methods, namely the learning vector quantization, radial basis function, the feedforward network that uses the conjugate gradient optimization algorithm, with the performance of the logistic regression and the standard backpropagation algorithm. All these methods are applied to a dataset of 139 matched-pairs of bankrupt and nonbankrupt US firms for the period 1983-1994. The results of this study indicate that the contemporary neural network methods applied in the present study provide superior results to those obtained from the logistic regression method and from the feedforward method using the standard backpropagation algorithm.

关键词： Artificial neural networks Predictive models Intelligent networks Logistics backpropagation algorithms Statistical analysis Neural networks Feedforward neural networks Vector quantization Optimization methods

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Medium term electric load forecasting using artificial neural networks

Medium term electric load forecasting using artificial neura...

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International Conference on Electric Power Engineering, PowerTech Budapest

作者： L. Varga Hungarian Power Companies Limited Hungary

In the paper artificial neural networks are applied to medium term electric load forecasting for the Hungarian Electric Power System. A feedforward multi-layer network with one hidden layer was applied using the error backpropagation algorithm for the estimation of the model weights. A model description and detailed prediction error analysis are presented.

关键词： Load forecasting Artificial neural networks Predictive models Power system modeling backpropagation algorithms Power system planning Neural networks Load modeling Weather forecasting Error analysis

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Recurrent neural gas in electric load forecasting

Recurrent neural gas in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha V.N.A.L. da Silva G.F. Ribeiro Systems Engineering and Computer Science Program-Graduate Programs in Engineering Federal University of Rio de Janeiro Brazil Electric Power Research Center Brazil

We have proposed for the task of hourly electric load forecasting a hybrid neural system combining unsupervised and supervised learning. The system consists of a recurrent neural gas (RNG) network and many Elman neural networks (ENs). RNG is a modification we introduced in the neural gas (NG) network in order to enable it to do clustering using a sequence of input data. For verifying the RNG's performance, many architectures are compared in the learning of global and local models. In a global model only one supervised network is trained and in a local model the training examples are grouped by a clustering algorithm and each one of these groups is sent to different supervised networks. These architectures use different clustering algorithms (NG and RNG) or different supervised networks for prediction (ENs that are trained by backpropagation or backpropagation through time, and feedforward networks).

关键词： Load forecasting Clustering algorithms Neural networks Predictive models Power engineering and energy backpropagation algorithms Supervised learning Systems engineering and theory Computer science Recurrent neural networks

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