상세정보
학술지
Linguistic Models as a Framework of User-Centric System Modeling
- 저자
- W. Pedrycz, 곽근창
- 발행일
- 200607
- 출처
- IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, v.36 no.4, pp.727-745
- ISSN
- 1083-4427
- 협약과제
- 06MI1200, URC를 위한 내장형 컴포넌트 기술개발 및 표준화, 조영조
- 초록
- In this paper, the fundamental idea of linguistic models introduced by Pedrycz and Vasilakos (1999) is followed and their comprehensive design framework is developed. The paradigm of linguistic modeling is concerned with constructing models that: 1) are user centric and 2) inherently dwell upon collections of highly interpretable and user-oriented entities such as information granules. The objective of this paper is to investigate and compare alternative design options, present an organization of the overall optimization process, and come up with a specification of several evaluation mechanisms of the performance of the models. The underlying design tool guiding the development of linguistic models revolves around the augmented version of fuzzy clustering known as a context-based or conditional fuzzy C-means (C-FCM). The design process comprises several main phases such as: 1) defining and further refining context fuzzy sets; 2) completing conditional fuzzy clustering; and 3) optimizing parameters (connections) linking information granules in the input and output spaces. An iterative process of forming information granules in the input and output spaces is discussed. Their membership functions are adjusted by the gradient-based learning guided by the minimization of some performance index. The paper comes with a comprehensive suite of experiments that lead to some design guidelines of the models. Furthermore, the performance of linguistic models is contrasted with that of other fuzzy models, especially radial basis function neural networks (RBFNNs) and related constructs that are based on concepts of fuzzy clustering. © 2006 IEEE.
- KSP 제안 키워드
- Conditional Fuzzy C-means, Conditional fuzzy clustering, Context-based, Design process, Design tool, Fuzzy Sets, Fuzzy models, Gradient-based learning, Information granules, Iterative process, Membership Functions