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 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Design of T-S fuzzy classifier via linear matrix inequality approach. / Kim, Moon Hwan; Park, Jin Bae; Joo, Young Hoon; Lee, Ho Jae.
In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3613, No. PART I, 27.10.2005, p. 406-415.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Design of T-S fuzzy classifier via linear matrix inequality approach
AU - Kim, Moon Hwan
AU - Park, Jin Bae
AU - Joo, Young Hoon
AU - Lee, Ho Jae
PY - 2005/10/27
Y1 - 2005/10/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=26944454001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26944454001&partnerID=8YFLogxK
M3 - Conference article
VL - 3613
SP - 406
EP - 415
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
IS - PART I
ER -