Separable nonlinear least-squares methods for efficient off-line and on-line modeling of systems using Kautz and Laguerre filters

Kautz and Laguerre filters are effective linear regression models that can describe accurately an unknown linear system with a fewer parameters than finite-impulse response (FIR) filters. This is achieved by expanding the transfer functions of the Kautz and Laguerre filters around some a priori knowledge, concerning the dominating time constants or resonant modes of the system to be identified. When the estimation of these filters is based on a minimization of the least-squares error criterion, the minimization problem becomes separable with respect to the linear coefficients. Therefore, the original unseparated problem can be reduced to a separated problem in only the nonlinear poles, which is numerically better conditioned than the original unseparated one. This paper proposed batch and recursive algorithms that are derived using this separable nonlinear least-squares method, for the estimation of the coefficients and poles of Kautz and Laguerre filters. They have similar computational loads, but better convergence properties than their corresponding algorithms that solve the unseparated problem. The performance of the suggested algorithms is compared to alternative batch and recursive algorithms in some system identification examples. Generally, it is shown that the proposed batch and recursive algorithms have better convergence properties than the alternatives.