Abstract
In thispaper, a approach for automatically generating fuzzy rules from sample patterns is presented. Then a self-adaptive fuzzy neural network is built based on the fuzzy partition which divides the input space with input and output information. The salient characteristics of the self-adaptive fuzzy neural networks are:1) structure identification and parameters estimation are performed automatically and simultaneously ;2)fuzzy rules can be recruited or deleted dynamically;3)parameters of rules can be obtained by evolutionary computation. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive comparisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.
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© 2007 Springer-Verlag Berlin Heidelberg
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Liu, F. (2007). Study on Self-adaptive Fuzzy Neural Networks. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_38
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DOI: https://doi.org/10.1007/978-3-540-74282-1_38
Publisher Name: Springer, Berlin, Heidelberg
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