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检索条件"主题词=Backpropagation Algorithms"

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MultiLearner Based Recursive Supervised Training

MultiLearner Based Recursive Supervised Training

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Kiruthika Ramanathan Sheng Uei Guan Laxmi R Iyer Department of Electrical and Computer Engineering National University of Singapore Singapore

In supervised learning, most single solution neural networks such as constructive backpropagation give good results when used with some datasets but not with others. Others such as probabilistic neural networks (PNN) fit a curve to perfection but need to be manually tuned in the case of noisy data. Recursive percentage based hybrid pattern training (RPHP) overcomes this problem by recursively training subsets of the data, thereby using several neural networks. MultiLearner based recursive training (MLRT) is an extension of this approach, where a combination of existing and new learners are used and subsets are trained using the weak learner which is best suited for this subset. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the spam dataset and comparable performances on the vowel and the two-spiral problems

关键词： Neural networks backpropagation algorithms Supervised learning Neurons Clustering algorithms Genetics Testing Interpolation Machine learning Unsupervised learning

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Anti-Dumping Early-Warning System Based on Neuro-FDT

Anti-Dumping Early-Warning System Based on Neuro-FDT

引用

International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Jian-na Zhao Zhi-peng Chang School of business and Administration North China Electric Power University Baoding China

In this paper, a new anti-dumping early-warning system for the export of China's textile products is presented. The early-warning system based on neuro-fuzzy decision tree modeling method is different from traditional modeling methods. Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are poor in classification accuracy. Neural networks-fuzzy decision tree (a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT's classification accuracy and extracts more accuracy human interpretable classification rules. The other new attempt is the setting of early-warning intervals. The result of the positive research indicated that this system is very valid for anti-dumping prediction and it will have a good application prospect in this area

关键词： Decision trees Fuzzy sets Humans Classification tree analysis Textile industry Testing backpropagation algorithms Power system modeling Electronic mail Inspection

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Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Recurrent Fuzzy Neural Network Using Genetic Algorithm for L...

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： F. J. Lin P. K. Huang Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

A recurrent fuzzy neural network (RFNN) using genetic algorithm (GA) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an on-line training RFNN with backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a real-time GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN on-line. The theoretical analyses for the proposed RFNN using GA controller are described in detail. Finally, experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance

关键词： Fuzzy neural networks Genetic algorithms Induction motors Servomechanisms Fuzzy control Motion control backpropagation algorithms Convergence Error analysis Lyapunov method

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Algorithmic mapping of MLP network on Neural DF KPI architecture

Algorithmic mapping of MLP network on Neural DF KPI architec...

引用

International Conference on Computational Cybernetics (ICCC)

作者： Liberios Vokorokos Norbert Adam Jana Trelova Department of Computers and Informatics Technical University of Kosice Kosice Slovakia Computer Centre of FEEI Technical University of Koaice Kosice Slovakia

Artificial neural networks gain increasingly higher popularity in application fields with stress laid on acquiring information in real-time. Although there are various implementations of neural networks available for sequential computer systems, lots of these models require an enormous amount of time for the training phase of neural network in case of large network. Therefore the new time saving concepts were developed especially for time reduction of network training thus including modification of original models and learning algorithms as well as implementation of these models on parallel computer system. Conventional formulation of fundamental neural algorithms make implementation of neural networks on existing parallel hardware more difficult, therefore a solution for effective network parallelisation in the form of algorithmic mapping of multilayer feedforward neural network with backpropagation learning (FFBP) on massively parallel system neural DF KPI was searched in the framework of VEGA 1/1064/04 project performed on Department of Computers and Informatics at Technical University of Kosice.

关键词： Artificial neural networks Computer networks backpropagation algorithms Multi-layer neural network Concurrent computing Neural networks Feedforward neural networks Computer architecture Application software Stress

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Fingerprint feature extraction based discrete cosine transformation (DCT)

Fingerprint feature extraction based discrete cosine transfo...

引用

International Conference on Computing & Informatics (ICOCI)

作者： Chin Kim On Paulraj M. Pandiyan Sazali Yaacob Azali Saudi Intelligent System and Control International Conference on Emerging Technologies Coimbatore India

Fingerprint identification and verification are one of the most significant and reliable identification methods. It is impossible that two people have the same fingerprint. Automatics identification of humans based on fingerprint requires the input fingerprint to be match with a large number of fingerprints in the database. Generally, the fingerprint recognition systems are unable to solve the problem of rotated scanned input images. The classification systems are failed to classify the rotated scanned fingerprint image with the fingerprint image that store in the database, which both of the fingerprint images are actually belonging to the same person. In this paper, a simple and effectiveness algorithm is proposed for fingerprint image recognition and the proposed algorithm is able to solve the problem discussed above. The proposed algorithm involved two stages, which is pre-processing of fingerprint image and feature extraction based DCT. The extracted DCT data is used as input for the backpropagation neural network training for personal identification.

关键词： Fingerprint recognition Feature extraction Discrete cosine transforms Image matching backpropagation algorithms Image databases Humans Image recognition Data mining Neural networks

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Flexible neuro-fuzzy systems: Structures, learning and performance evaluation

引用

IEEE Transactions on Neural Networks 2006年第1期17卷 270-270页

作者： Gaweda, A.E. Department of Medicine University of Louisville Louisville KY 40292 United States

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Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks

Comparison of particle swarm optimization and backpropagatio...

引用

IEEE Swarm Intelligence Symposium, SIS

作者： V.G. Gudise G.K. Venayagamoorthy Department of Electrical and Computer Engineering University of Missouri Rolla USA

Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm.

关键词： Particle swarm optimization backpropagation algorithms Neural networks Feedforward neural networks Artificial neural networks Computer networks Pattern recognition Educational institutions Neurons Venus

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Face recognition based on constructive neural networks covering learning algorithm

Face recognition based on constructive neural networks cover...

引用

American Control Conference 2005 (ACC)

作者： Huang, GH Xiong, ZH Shao, HH Shanghai Jiao Tong Univ Inst Automat Shanghai 200030 Peoples R China

ISBN: (纸本)0780390989

A general and efficient design approach using covering neural classifier to cope with the high-dimensional and small sample size problem is proposed. For alleviating the computational burden, face features are first extracted by the principal component analysis (PCA). In order to avoid the influence of outlier classes and reduce the large overlapping of neighboring classes, a new weighted Fisher linear discriminant (WFLD) criterion is presented by weighting contributions of individual class pairs according to the Euclidian distance of the respective class means. A new learning algorithm is used to train the neural networks classifier, which uses the "sphere neighborhoods" to cover the input samples and draw up their distributions in the original space. Thus, the training problem of neural networks may be transformed into the covering problem of a point set, which avoids the iterative process, and overcomes the problem of longtime training of classical neural networks. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.

关键词： Face recognition Neural networks Feature extraction Principal component analysis Shape measurement Data mining backpropagation algorithms Automation Position measurement Lighting

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Automatic keyphrases extraction from document using backpropagation

Automatic keyphrases extraction from document using backprop...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jia-Bing Wang Hong Peng Jing-Song Hu School of Computer Science and Engineering South China University of Technology Guangzhou China

Automatic keyphrase extraction from documents is a task with many applications in information retrieval and natural language processing. Previously, Several keyphrase extraction methods have been proposed based on different techniques. In this paper a keyphrase extraction approach based on backpropagation is proposed. In order to determine whether a phrase is a keyphrase or not, the following features of a phrase in a given document are adopted: its term frequency TF and inverted document frequency IDF, whether or not it appears in the title or headings (subheadings) of the given document, and its distribution in the paragraphs of the given document. The algorithm is evaluated by the standard information retrieval metrics of precision and recall and human assessment. Experiment results show that this approach is competitive with other known methods.

关键词： Data mining Information retrieval Natural language processing Databases Frequency measurement Particle measurements Computer science Application software backpropagation algorithms Humans

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A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM

A comparison of PSO and backpropagation for training RBF neu...

引用

IEEE Swarm Intelligence Symposium, SIS

作者： S. Mohaghegi Y. del Valle G.K. Venayagamoorthy R.G. Harley School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Professor Emeritus University of KwaZulu-Natal South Africa

backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.

关键词： Neural networks Power systems Automatic voltage control Artificial neural networks backpropagation algorithms Convergence Radial basis function networks Particle swarm optimization STATCOM Robustness

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