A New Framework for Fuzzy Modeling Using Genetic Algorithm
Seiichi Matsushita*, Takeshi Furuhashi** and Hiroaki Tsutsui***
*Nagoya Municipal Industrial Research Institute
**Dept. of Information Electronics, Nagoya University
This paper presents a new framework for fuzzy modeling using genetic algorithm. A model of actual object in the real world should satisfy various criteria, such as precision, generality, noise immunity, etc. It has been difficult for the fuzzy modeling to allocate proper weights on these criteria. The framework introduced in this paper consists of a model generation block and a model-testing block. The model generation block generates candidates of fuzzy model under criteria with higher importance, and the model-testing block tests the candidates under notso-important criteria. This division of criteria can put emphasis on the criteria in the generation block and less on those in the testing block. Simulations are done to show the effectiveness of the proposed framework.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.