NDLI: Improving performance of radial basis function network based with particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Qasem, S.N. Shamsuddin, S.M.H.
Copyright Year	2009
Description	Author affiliation: Soft Computing Research Group, Faculty of Computer Science and Information System, University Technology Malaysia, Skudai, Johor, Malaysia (Qasem, S.N.; Shamsuddin, S.M.H.)
Abstract	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. This study proposes hybrid learning of RBF Network with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. The hybrid learning of RBF Network 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 hybrid learning of RBF Network 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) illustrate the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.
Starting Page	3149
Ending Page	3156
File Size	495253
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983342
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Particle swarm optimization Clustering algorithms Convergence Error analysis Unsupervised learning Parameter estimation Supervised learning Least squares approximation Testing Unsupervised and supervised learning component Hybrid learning Radial basis function network K-means Least mean squares Backpropogation
Content Type	Text
Resource Type	Article

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