Abstract
A new algorithm of fuzzy neural network learning is presented. It is based on combining genetic algorithm of hierarchical structure with evolution programming. This algorithm is used to optimize the structure and parameters of fuzzy neural network, reject redundant nodes and redundancy connections, and improve the treatment ability of the network. The results of analysis and experiment show that, by using this method the fuzzy neural network of mechanical fault diagnosis has good concise structure and diagnosis effect.
This work is financed by National Natural Science Foundation of China #50975044 to #50875175.
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© 2011 Springer-Verlag Berlin Heidelberg
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Hao, Y., Ren, Z., Wang, B. (2011). Research on Hierarchical Genetic Algorithm Optimized Based on Fuzzy Neural Network. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_72
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DOI: https://doi.org/10.1007/978-3-642-23220-6_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23219-0
Online ISBN: 978-3-642-23220-6
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