TY - GEN
T1 - Dynamic solution agent algorithm for TSK-type interval-valued fuzzy system optimization
AU - Lee, Ching Hung
AU - Kuo, Che Ting
PY - 2012
Y1 - 2012
N2 - In this paper, a new heuristic learning algorithm, the modified water flow-like algorithm (MWFA), is proposed for TSK-type interval-valued fuzzy system (TIVFS) optimization. The water flow-like algorithm (WFA) is inspired by the natural behavior of water flows; the splitting, moving, merging, evaporation, and precipitation operations were developed for optimization. However, the original WFA is not valid for the continuous optimization problem. Therefore, some modifications, including some moving strategies, such as applying the tabu searching and gradient-descent techniques, are proposed to enhance the performance of MWFA. In addition, the modified strategies in evaporation and precipitation operations are more consistent with the natural behavior of water flows and they increase the diversity of solution agents. Finally, the MWFA is applied in the design of TIVFS for nonlinear system identification to demonstrate the effectiveness and performance.
AB - In this paper, a new heuristic learning algorithm, the modified water flow-like algorithm (MWFA), is proposed for TSK-type interval-valued fuzzy system (TIVFS) optimization. The water flow-like algorithm (WFA) is inspired by the natural behavior of water flows; the splitting, moving, merging, evaporation, and precipitation operations were developed for optimization. However, the original WFA is not valid for the continuous optimization problem. Therefore, some modifications, including some moving strategies, such as applying the tabu searching and gradient-descent techniques, are proposed to enhance the performance of MWFA. In addition, the modified strategies in evaporation and precipitation operations are more consistent with the natural behavior of water flows and they increase the diversity of solution agents. Finally, the MWFA is applied in the design of TIVFS for nonlinear system identification to demonstrate the effectiveness and performance.
KW - gradient-descent method
KW - Heuristic algorithm
KW - interval-valued fuzzy system
KW - system identification
KW - tabu search
UR - http://www.scopus.com/inward/record.url?scp=84871689422&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2012.6360922
DO - 10.1109/ICIEA.2012.6360922
M3 - Conference contribution
AN - SCOPUS:84871689422
SN - 9781457721175
T3 - Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
SP - 1297
EP - 1302
BT - Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
Y2 - 18 July 2012 through 20 July 2012
ER -