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Computational Intelligence and Neuroscience
Volume 2014, Article ID 982045, 6 pages
http://dx.doi.org/10.1155/2014/982045
Research Article

A New Optimized GA-RBF Neural Network Algorithm

1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
2School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China
3Changzhou College of Information Technology, Changzhou 213164, China

Received 13 June 2014; Revised 25 August 2014; Accepted 1 September 2014; Published 13 October 2014

Academic Editor: Daoqiang Zhang

Copyright © 2014 Weikuan Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer’s neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.