Abstract
This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model [1], because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model [2] because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used [3], which is a modified version of fuzzy C-means (FCM) [4]. In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 328-337 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Fuzzy Systems |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1997 |
Bibliographical note
Funding Information:Manuscript received April 22, 1995; revised October 21, 1996. This work was supported by the Ministry of Information and Communication (MIC), Seoul, Korea. E. Kim, S. Ji, and M. Park are with the Department of Electronic Engineering, Yonsei University, Seoul, 120-749 Korea. M. Park is with the Department of Electronic Engineering, Seoul National Polytechnic University, Seoul, 139-743 Korea. Publisher Item Identifier S 1063-6706(97)04840-6.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics