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http://dx.doi.org/10.5762/KAIS.2011.12.11.5164

Nonlinear Characteristics of Fuzzy Inference Systems by Means of Individual Input Space  

Park, Keon-Jun (Department of Information Communication Engineering, Wonkwang University)
Lee, Dong-Yoon (Department of Electrical Electronic Engineering, Joongbu University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.12, no.11, 2011 , pp. 5164-5171 More about this Journal
Abstract
In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.
Keywords
Fuzzy Inference Systems; Individual Input Space; Membership Functions; Nonlinear Characteristics;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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