Generality and Conciseness of Submodels in Hierarchical Fuzzy Modeling

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.KeywordsGenetic AlgorithmMembership FunctionFuzzy RuleInput SpaceFuzzy ModelingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.