Evolutionary optimization of fuzzy systems using chained possibilistic rule graph encoding = 연쇄적 가능성 규칙 그래프 인코딩을 이용한 퍼지 시스템의 진화 연산 최적화

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Fuzzy systems have been utilized and developed in many application fields and proven their usefulness for their unique feature that they solve the given problem with human understandable linguistic rules. As the design of a fuzzy system requires human experts`` knowledge which frequently becomes expensive and difficult, designing a fuzzy system solely based on relevant data by some optimization methods has been the main issue in literature. Especially, evolutionary algorithms receive much attention due to their vigorous and comprehensive optimization capabilities. Solving fuzzy system design problems using evolutionary algorithms demands three factors: an encoding scheme which can efficiently encode/decode candidate fuzzy systems into/from chromosomes, evaluation criteria which can guide candidate solutions into promising directions, and evolutionary operations which can evolve the given chromosomes into better form of solutions so that better fuzzy systems can be found as evolution proceeds. At the same time, those three factors should be designed considering three aspects of fuzzy systems: performance, compactness, and interpretability. This thesis proposes an automatic method to design fuzzy systems considering those requirements. The proposed method solves the fuzzy system design problem as a multi objective parameter optimization problem. For this purpose, this thesis firstly proposes a chained possibilistic rule graph encoding scheme which interpret premises of a fuzzy system as a graph. Vertices and edges of the chained possibilistic rule graph represents parameters of antecedent membership functions and structure variation, which enables simultaneous optimization of structure and parameters of fuzzy systems effectively. Secondly, five evaluation criteria which consider performance, compactness, and interpretability of fuzzy systems are developed. Especially, two new measures which constrain distribution of antecedent membership functions in phenoty...
Lee, Ju-Jangresearcher이주장researcher
한국과학기술원 : 전기및전자공학전공,
Issue Date
258136/325007  / 000995056

학위논문(박사) - 한국과학기술원 : 전기및전자공학전공, 2006.8, [ xiii, 133 p. ]


Fuzzy Controller Design; Fuzzy Classification Design; Fuzzy Modeling; Evolutionary Algorithm; Multi Objective Optimization; Fuzzy System; Interpretability of Fuzzy Systems; 퍼지 시스템의 이해성; 퍼지 제어기 설계; 퍼지 분류기 설계; 퍼지 모델링; 진화 연산 알고리즘; 다목적 최적화 이론; 퍼지 시스템

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