Abstract
The system composed of various implication-based neuro-fuzzy networks in one parallel structure is proposed in this paper. Different phases of data processing are distinguished, i.e. learning, testing, and problem solving. A competetive learning of the neuro-fuzzy networks is employed. This learning method refers to the first layer, which is the same in every network. The system with fuzzy parameters of membership functions is also considered. In this case, the neuro-fuzzy architectures may be viewed as fuzzy inference neural networks with fuzzy parameters, and treated analogously to fuzzy neural networks.
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Rutkowska, D., Nowicki, R., Hayashi, Y. (2002). Parallel Processing by Implication-Based Neuro-Fuzzy Systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2001. Lecture Notes in Computer Science, vol 2328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48086-2_66
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DOI: https://doi.org/10.1007/3-540-48086-2_66
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