Identification of Quasi-ARX Neurofuzzy Model with an SVR and GA Approach

Yu CHENG  Lan WANG  Jinglu HU  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E95-A   No.5   pp.876-883
Publication Date: 2012/05/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E95.A.876
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Systems and Control
quasi-ARX neurofuzzy networks,  support vector regression,  genetic algorithm,  input selection,  

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The quasi-ARX neurofuzzy (Q-ARX-NF) model has shown great approximation ability and usefulness in nonlinear system identification and control. It owns an ARX-like linear structure, and the coefficients are expressed by an incorporated neurofuzzy (InNF) network. However, the Q-ARX-NF model suffers from curse-of-dimensionality problem, because the number of fuzzy rules in the InNF network increases exponentially with input space dimension. It may result in high computational complexity and over-fitting. In this paper, the curse-of-dimensionality is solved in two ways. Firstly, a support vector regression (SVR) based approach is used to reduce computational complexity by a dual form of quadratic programming (QP) optimization, where the solution is independent of input dimensions. Secondly, genetic algorithm (GA) based input selection is applied with a novel fitness evaluation function, and a parsimonious model structure is generated with only important inputs for the InNF network. Mathematical and real system simulations are carried out to demonstrate the effectiveness of the proposed method.