Paper Info
Title
Identification of Fuzzy Inference System Based on Information Granulation
Abstract
In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads not only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems 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 four representative numerical examples such as No. n-linear function, gas furnace, NO. x emission process data, 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.
Year
DOI
Venue
2010
10.3837/tiis.2010.08.008
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Keywords
Field
DocType
Space search algorithm (SSA),particle swarm algorithm (PSO),information granulation (IG),fuzzy inference system (FIS)
Mathematical optimization,Defuzzification,Global optimization,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Adaptive neuro fuzzy inference system,Fuzzy number,Membership function
Journal
Volume
Issue
ISSN
4
4
1976-7277
Citations 
PageRank 
References 
7
0.66
13
Authors
5
Name
Order
Citations
PageRank
Wei Huang1164.27
Lixin Ding218825.22
Sung-Kwun Oh3109895.12
Chang-Won Jeong4547.39
Su-Chong Joo510513.18