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A Measurement Fusion Method for Nonlinear System Identification Using a Cooperative Learning Algorithm

ysxia2001{at}yahoo.com College of Mathematics and Computer Science, Fuzhou University, 350002, Fuzhou, China

Mohamed S. Kamel

mkamel{at}pami.uwaterloo.ca Department of Electrical and Computer Engineering, University of Waterloo, Waterloo N2L 3G1, Canada

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.