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System identification is an important issue in communication, instrumentation, and control systems. In this paper, we proposed a method with higher-order cumulant fitting for nonlinear system identification. Compared with the conventional method, which uses second-order cumulant as a constraint, the proposed method uses fourth-order cumulant in order to smooth out the additive Gaussian noise. Since the cost function with higher-order statistics has local minima, we also propose to use a hybrid method of simplex and genetic algorithms to minimize the cost function. The applicability of the proposed method is demonstrated by the computer simulations.
Instrumentation and Measurement, IEEE Transactions on (Volume:52 , Issue: 3 )
Date of Publication: June 2003