Abstract
By utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic and neural network, a new self-constructing fuzzy wavelet neural networks (SCFWNN) using compensatory fuzzy operators are proposed for intelligent fault diagnosis. An on-line learning algorithm is applied to automatically construct the SCFWNN. There are no rules initially in the SCFWNN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The proposed SCFWNN is much more powerful than either the neural network or the fuzzy system since it can incorporate the advantages of both. The results of simulation show that this SCFWNN method has the advantage of faster learning rate and higher diagnosing precision.
Foundation item: Project supported by the National High-Tech. R&D Program for CIMS, China (Grant No. 2003AA414210).
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Yu, H., Guo, Q., Xu, A. (2005). A Self-constructing Compensatory Fuzzy Wavelet Network and Its Applications. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_93
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DOI: https://doi.org/10.1007/11539506_93
Publisher Name: Springer, Berlin, Heidelberg
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