Abstract
A linear matrix inequality approach to designing accurate classifier with a compact T-S(Takagi-Sugeno) fuzzy-rule is proposed, in which all the elements of the T-S fuzzy classifier design problem have been moved in parameters of a LMI optimization problem. Two-step procedure is used to effectively design the T-S fuzzy classifier with many tuning parameters: antecedent part and consequent part design. Then two LMI optimization problems are formulated in both parts and solved efficiently by using interior-point method. Iris data is used to evaluate the performance of the proposed approach. From the simulation results, the proposed approach showed superior performance over other approaches.
| Original language | English |
|---|---|
| Pages (from-to) | 406-415 |
| Number of pages | 10 |
| Journal | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Volume | 3613 |
| Issue number | PART I |
| Publication status | Published - 2005 Oct 27 |
| Event | Second International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Kim, M. H., Park, J. B., Joo, Y. H., & Lee, H. J. (2005). Design of T-S fuzzy classifier via linear matrix inequality approach. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3613(PART I), 406-415.