Iterative identification methods for input nonlinear multivariable systems using the key-term separation principle

Abstract Identification for input nonlinear multivariable systems lies in that there exist the products of the parameters of the nonlinear block and the linear block. By means of the key-term separation principle, a subsystem least squares based iterative algorithm and a subsystem gradient based iterative algorithm are proposed for input nonlinear systems described by controlled autoregressive moving average models. Finally, the proposed methods are tested using numerical examples.

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