LBG-Based Non-fuzzy Inference System for Nonlinear Process
We introduce a non-fuzzy inference system based on Linde-Buzo-Gray (LBG) Algorithm to construct model for nonlinear process. In grid partition, the generation of fuzzy rules has the problem that the number of fuzzy rules exponentially increases. To solve this problem, we generate the subspaces using the scatter partition of input space. The rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using LBG algorithm. The consequence part of the rules is represented in the form of polynomial functions. The data widely used in nonlinear process is used to evaluate the performance of the proposed model.
KeywordsNon-fuzzy inference systems Linde-buzo-gray (LBG) algorithm Hard scatter partition Rule generation Nonlinear characteristics
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835).
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