Abstract
In the paper the evolutionary strategy is used for learning of neuro-fuzzy structures of a Mamdani type applied to modelling of nonlinear systems. In the process of evolution we determine parameters of fuzzy membership functions, specific t-norm in a fuzzy inference, specific t-norm for aggregation of antecedents in each rule, and specific t-conorm describing an aggregation operator. The method is tested using well known approximation benchmarks.
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Cpałka, K., Rebrova, O., Nowicki, R., Rutkowski, L. (2011). On Designing of Flexible Neuro-Fuzzy Systems for Nonlinear Modelling. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_24
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DOI: https://doi.org/10.1007/978-3-642-21881-1_24
Publisher Name: Springer, Berlin, Heidelberg
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