Abstract
Hierarchical fuzzy modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple inputs. This paper presents a new method of dividing input spaces for hierarchical fuzzy modeling using Fuzzy Neural Network (FNN) and Genetic Algorithm (GA). Uneven division of input space for each submodel in the hierarchical fuzzy model can be achieved with the proposed method. The obtained hierarchical fuzzy models are probable to be more concise and more precise than those identified with the conventional methods. Studies on effects of the weights on performance indices for the fuzzy model are also shown in this paper.
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© 1999 Springer-Verlag Berlin Heidelberg
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Tachibana, K., Furuhashi, T. (1999). Generality and Conciseness of Submodels in Hierarchical Fuzzy Modeling. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_28
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DOI: https://doi.org/10.1007/3-540-48873-1_28
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