Abstract
The central problem in training a radial basis function neural network is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose a new method to construct an adaptive RBF neural network classifier based on artificial immune network algorithm. A multiple granularities immune network (MGIN) algorithm is employed to get the candidate hidden neurons and construct an original RBF network including all candidate neurons, and a removing redundant neurons procedure is used to simplify the classifier finally. Some experimental results show that the network obtained tends to generalize well.
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© 2007 Springer-Verlag Berlin Heidelberg
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Li, Z., Zhong, J., Feng, Y., Wu, Z. (2007). An Adaptive Classifier Based on Artificial Immune Network. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_48
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DOI: https://doi.org/10.1007/978-3-540-74771-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74770-3
Online ISBN: 978-3-540-74771-0
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