Hybrid learning enhancement of RBF network with particle swarm optimization
This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden laye...
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my.utm.145502017-08-13T01:05:51Z http://eprints.utm.my/id/eprint/14550/ Hybrid learning enhancement of RBF network with particle swarm optimization Noman, Sultan Shamsuddin, Siti Mariyam Hassanien, Aboul Ella HD28 Management. Industrial Management This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation. Springer 2009 Book Section PeerReviewed Noman, Sultan and Shamsuddin, Siti Mariyam and Hassanien, Aboul Ella (2009) Hybrid learning enhancement of RBF network with particle swarm optimization. In: Foundations of Computational Intelligence Volume 1: Learning and Approximation. Springer, Berlin/ Heidelberg, pp. 381-397. ISBN 978-3-642-01081-1 |
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HD28 Management. Industrial Management Noman, Sultan Shamsuddin, Siti Mariyam Hassanien, Aboul Ella Hybrid learning enhancement of RBF network with particle swarm optimization |
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This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation. |
format |
Book Section |
author |
Noman, Sultan Shamsuddin, Siti Mariyam Hassanien, Aboul Ella |
author_facet |
Noman, Sultan Shamsuddin, Siti Mariyam Hassanien, Aboul Ella |
author_sort |
Noman, Sultan |
title |
Hybrid learning enhancement of RBF network with particle swarm optimization |
title_short |
Hybrid learning enhancement of RBF network with particle swarm optimization |
title_full |
Hybrid learning enhancement of RBF network with particle swarm optimization |
title_fullStr |
Hybrid learning enhancement of RBF network with particle swarm optimization |
title_full_unstemmed |
Hybrid learning enhancement of RBF network with particle swarm optimization |
title_sort |
hybrid learning enhancement of rbf network with particle swarm optimization |
publisher |
Springer |
publishDate |
2009 |
url |
http://eprints.utm.my/id/eprint/14550/ |
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1643646420375306240 |
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13.164666 |