Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation |
| |
Authors: | Jeoung-Nae Choi Sung-Kwun Oh Witold Pedrycz |
| |
Affiliation: | 1. Department of Electrical Engineering, Daelim college, 526-7 Bisan-dong, Dongan_gu, Anyang-si, Gyeonggi-do 431-715, South Korea;2. Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do 445-743, South Korea;3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6;4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland |
| |
Abstract: | The paper is concerned with a hybrid optimization of fuzzy inference systems based on hierarchical fair competition-based parallel genetic algorithms (HFCGA) and information granulation. The process of information granulation is realized with the aid of the C-Means clustering. HFCGA being a multi-population based parallel genetic algorithms (PGA) is exploited here to realize structure optimization and carry out parameter estimation of the fuzzy models. The HFCGA becomes helpful in the context of fuzzy models as it restricts a premature convergence encountered quite often in optimization problems. It concerns a set of parameters of the model including among others the number of input variables to be used, a specific subset of input variables, and the number of membership functions. In the hybrid optimization process, two general optimization mechanisms are explored. The structural development of the fuzzy model is realized via the HFCGA optimization and C-Means, whereas to deal with the parametric optimization we proceed with a standard least square method and the use of the HFCGA technique. A suite of comparative studies demonstrates that the proposed algorithm leads to the models whose performance is superior in comparison with some other constructs commonly used in fuzzy modeling. |
| |
Keywords: | Fuzzy relation model Information granulation Genetic algorithms Hierarchical fair competition (HFC) C-Means Multi-population genetic optimization |
本文献已被 ScienceDirect 等数据库收录! |
|