Weighted fuzzy interpolative reasoning based on weighted increment transformation and weighted ratio transformation techniques

  • Authors:

  • Shyi-Ming Chen;Yaun-Kai Ko;Yu-Chuan Chang;Jeng-Shyang Pan

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan

  • Venue:
  • IEEE Transactions on Fuzzy Systems
  • Year:
  • 2009

Quantified Score

Hi-index 0.02

Visualization

Abstract

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. The proposed method uses weighted increment transformation and weighted ratio transformation techniques to handle weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. It allows each variable that appears in the antecedent parts of fuzzy rules to associate with a weight between zero and one. Moreover, we also propose an algorithm that automatically tunes the optimal weights of the antecedent variables appearing in the antecedent parts of fuzzy rules. We also apply the proposed weighted fuzzy interpolative reasoning method to handle the truck backer-upper control problem. The proposed weighted fuzzy interpolative reasoning method performs better than the ones obtained by the traditional fuzzy inference system (2000), Huang and Shen's method (2008), and Chen and Ko's method (2008). The proposed method provides us with a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.