This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.
Bibliographical noteFunding Information:
* Corresponding author. Tel.: +82-2-970-6464; fax: +82-2-970-6480; e-mail: firstname.lastname@example.org. i This work was supported by Ministry of Information and Communication, Project 96074-IT1-I1.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence