Abstract
We introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. The gHFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning.
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© 2005 Springer-Verlag Berlin Heidelberg
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Oh, SK., Pedrycz, W., Kim, HK., Kim, YK. (2005). Genetically Optimized Hybrid Fuzzy Neural Networks with the Aid of TSK Fuzzy Inference Rules and Polynomial Neural Networks. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_50
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DOI: https://doi.org/10.1007/11494669_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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