Abstract
In this paper, we introduce the analysis and design of Information granulation based genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (IG_gHSOFPNN) by evolutionary optimization. The architecture of the resulting IG_gHSOFPNN results from a synergistic usage of the hybrid system generated by combining fuzzy polynomial neurons (FPNs)-based Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) with polynomial neurons (PNs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The augmented IG_gHSOFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HSOFPNN. The GA-based design procedure being applied at each layer of IG_gHSOFPNN leads to the selection of preferred nodes available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.
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Park, HS., Ahn, TC. (2006). The Analysis and Design of IG_gHSOFPNN by Evolutionary Optimization. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_26
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DOI: https://doi.org/10.1007/11881599_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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