Abstract
After analyzing the classification and training ability of a wavelet neural network (WNN), a novel WNN learning scheme integrating immunity based evolutionary algorithm (IDEA) is proposed, in which, IDEA is an evolutionary algorithm with an embedded immune mechanism. When WNN is used as a classifier, the process of seeking the least mean-square error (LMS) of an optimal problem is equivalent to that of finding the wavelet feature with maximal separability, namely, maximizing its separable division. On the other hand, with the capability of robust learning of its evolutionary process, IDEA is able to eliminate local degenerative phenomenon due to blindfold behaviors of original operators in the existing evolutionary algorithms. In the case of the twin-spiral problem, experimental simulation shows the feasibility of WNN training with the IDEA based learning algorithm.
This research is supported by National Science Foundation of China under grant no60133010.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, L., Nie, Y., Nie, W., Jiao, L. (2005). A Novel Classifier with the Immune-Training Based Wavelet Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_2
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DOI: https://doi.org/10.1007/11427445_2
Publisher Name: Springer, Berlin, Heidelberg
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