Self-Organizing Fuzzy Modeling Based on Hyperplane-Shaped Clusters

다차원 평면 클러스터를 이용한 자기 구성 퍼지 모델링

  • Koh, Taek-Beom (Dept.of Computer Electronics Engineering, Gyeongju University)
  • 고택범 (경주대학교 컴퓨터전자공학부)
  • Published : 2001.12.01

Abstract

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

Keywords

References

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