Abstract
This paper proposes a modified algorithm in Takagi-Sugeno (T-S) fuzzy modelling for a complex nonlinear system. First, the Gaussian kernel based fuzzy c-mean clustering method is presented to find a scalar offset of the linear function. Secondly, the fuzzy c-regression model (FCRM) and the weighted recursive least square (WRLS) algorithm are adopted to calculate the parameters of the linear model according to individual input variables. Finally, the proposed algorithm stops if the objective function is minimized, otherwise, returns to the first step. Two simulation examples are provided to demonstrate the effectiveness of the proposed T-S fuzzy modelling approach.
| Original language | English |
|---|---|
| Title of host publication | New Trends on System Sciences and Engineering - Proceedings of ICSSE 2015 |
| Editors | Hamido Fujita, Shun-Feng Su |
| Publisher | IOS Press BV |
| Pages | 536-546 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781614995210 |
| DOIs | |
| State | Published - 2015 |
| Externally published | Yes |
| Event | International Conference on System Science and Engineering, ICSSE 2015 - Morioka, Japan Duration: 06 07 2015 → 08 07 2015 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Volume | 276 |
| ISSN (Print) | 0922-6389 |
| ISSN (Electronic) | 1879-8314 |
Conference
| Conference | International Conference on System Science and Engineering, ICSSE 2015 |
|---|---|
| Country/Territory | Japan |
| City | Morioka |
| Period | 06/07/15 → 08/07/15 |
Bibliographical note
Publisher Copyright:© 2015 The authors and IOS Press. All rights reserved.
Keywords
- Fuzzy c-mean
- Takagi-Sugeno fuzzy model
- fuzzy c-regression model