Abstract
In this study, we introduce a concept of Rule-based fuzzy polynomial neural networks(RFPNN), a hybrid modeling architecture combining rule-based fuzzy neural networks(RFNN) and polynomial neural networks(PNN). We discuss their comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence(CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the Medical Imaging System(MIS) dataset.
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Oh, SK., Peters, J.F., Pedrycz, W., Ahn, TC. (2003). Genetically Optimized Rule-Based Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_73
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DOI: https://doi.org/10.1007/3-540-39205-X_73
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