A novel elliptical basis function neural networks optimized by particle swarm optimization

Authors:
Ji-Xiang Du;Chuan-Min Zhai;Zeng-Fu Wang;Guo-Jun Zhang
Affiliations:
Department of Automation, University of Science and Technology of China;Department of Mechanical Engineering, Hefei University;Department of Automation, University of Science and Technology of China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Learning Class Regions by the Union of Ellipsoids

ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472

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Abstract

In this paper, a novel model of elliptical basis function neural networks (EBFNN) is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. Finally, the experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.