A New Optimized GA-RBF Neural Network Algorithm
Joint Authors
Jia, Weikuan
Zhao, Dean
Su, Chunyang
Zhao, Yuyan
Shen, Tian
Hu, Chanli
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-13
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jia, Weikuan& Zhao, Dean& Shen, Tian& Su, Chunyang& Hu, Chanli& Zhao, Yuyan. 2014. A New Optimized GA-RBF Neural Network Algorithm. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1034661
Modern Language Association (MLA)
Jia, Weikuan…[et al.]. A New Optimized GA-RBF Neural Network Algorithm. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1034661
American Medical Association (AMA)
Jia, Weikuan& Zhao, Dean& Shen, Tian& Su, Chunyang& Hu, Chanli& Zhao, Yuyan. A New Optimized GA-RBF Neural Network Algorithm. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1034661
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034661