TY - GEN
T1 - Identification of T-S fuzzy classifier via linear matrix inequalities
AU - Kim, Moon Hwan
AU - Park, Jin Bae
AU - Kim, Weon Goo
AU - Joo, Young Hoon
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.
AB - In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method.
UR - http://www.scopus.com/inward/record.url?scp=33745593495&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745593495&partnerID=8YFLogxK
U2 - 10.1007/11589990_155
DO - 10.1007/11589990_155
M3 - Conference contribution
AN - SCOPUS:33745593495
SN - 3540304622
SN - 9783540304623
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1134
EP - 1137
BT - AI 2005
PB - Springer Verlag
T2 - 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
Y2 - 5 December 2005 through 9 December 2005
ER -