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Hybrid learning enhancement of RBF network with particle swarm optimization

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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Abstract

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.

Item Type:Book Section
Subjects:H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions:Management and Human Resource Development (Formerly known)
ID Code:14550
Deposited By: Siti Khairiyah Nordin
Deposited On:26 Aug 2011 11:20
Last Modified:13 Aug 2017 09:05

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