TY - GEN
T1 - A experimental study on space search algorithm in ANFIS-based fuzzy models
AU - Huang, Wei
AU - Ding, Lixin
AU - Oh, Sung Kwun
PY - 2010
Y1 - 2010
N2 - In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of ANFIS-based fuzzy models based on SSA and information granulation (IG). The overall hybrid identification of ANFIS-based fuzzy models comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using two representative numerical examples such as gas furnace, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.
AB - In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of ANFIS-based fuzzy models based on SSA and information granulation (IG). The overall hybrid identification of ANFIS-based fuzzy models comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using two representative numerical examples such as gas furnace, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.
KW - ANFIS-based fuzzy inference system
KW - Information granulation
KW - Particle swarm algorithm
KW - Space search algorithm
UR - http://www.scopus.com/inward/record.url?scp=77954444272&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13278-0_26
DO - 10.1007/978-3-642-13278-0_26
M3 - Conference contribution
AN - SCOPUS:77954444272
SN - 3642132774
SN - 9783642132773
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 206
BT - Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings
T2 - 7th International Symposium on Neural Networks, ISNN 2010
Y2 - 6 June 2010 through 9 June 2010
ER -