A reduced complexity algorithm, which is an adaptive version of the matching pursuit algorithm, is proposed for the identification of nonlinear systems. The algorithm is demonstrated on various nonlinear systems presented in the literature. The results are favorable compared to other nonlinear identification methods (e.g neural nets). It is demonstrated that the algorithm could be used for the design of adaptive controllers.
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