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Design of Type-2 FCM-based Fuzzy Inference Systems and Its Optimization

Type-2 FCM 기반 퍼지 추론 시스템의 설계 및 최적화

  • Received : 2011.01.06
  • Accepted : 2011.07.08
  • Published : 2011.11.01

Abstract

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

Keywords

Fuzzy inference system;Type-2 fuzzy C-means clustering;Scatter partition;Genetic optimization;Successive tuning method

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Acknowledgement

Grant : U-city 보안감시 기술협력센터

Supported by : 한국연구재단, 경기도