Uneven Input Space Division and Balance of Generality and Conciseness of Submodels for Hierarchical Fuzzy Modeling
Kanta Tachibana* and Takeshi Furuhashi**
*Complex System Engineering Laboratory, Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University
**Bioelectronics Laboratory, department of Information Electronics Graduate School of Engineering, Nagoya University
Received:March 15, 1999Accepted:January 1, 1970Published:March 20, 2000
Keywords:Hierarchical fuzzy modeling, Input space division, Generality of model, Conciseness of model
Hierarchical fuzzy modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple input. This paper presents a new method of dividing input spaces for hierarchical fuzzy modeling using the 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 likely more concise and more precise than those identified with conventional methods. Studies on effects of the weights on performance indices of generality and conciseness of the fuzzy model are also shown in this paper.
Cite this article as:K. Tachibana and T. Furuhashi, “Uneven Input Space Division and Balance of Generality and Conciseness of Submodels for Hierarchical Fuzzy Modeling,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.2, pp. 152-157, 2000.Data files: