Abstract
The fuzzy wavelet neural networks (FWNN) are proposed in this paper. The structure and two learning algorithms of the FWNN for R-F function are given. Under the framework of such structure and two learning algorithms, wavelet-based fuzzy neural networks for interval estimation of processed data and for interpolation of fuzzy if-then rules are proposed too. The simulation results are given to prove their feasibility.
This project is partly supported by the National Fund of Intercent. Expert and partly supported by the“863” National Fund.
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© 1999 Springer Science+Business Media New York
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Jiao, L.C., Liu, F., Wang, L., Zhang, Y.N. (1999). Fuzzy Wavelet Neural Networks: Theory and Applications. In: Chen, G., Ying, M., Cai, KY. (eds) Fuzzy Logic and Soft Computing. The International Series on Asian Studies in Computer and Information Science, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5261-1_9
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DOI: https://doi.org/10.1007/978-1-4615-5261-1_9
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