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Adaptive Estimation for Quantized Nonlinear Cascade System

In this paper, we introduce an adaptive estimation method for quantized nonlinear cascade system using moving window theory. Firstly, by force of the sub-decomposition technique, the considered system is transformed to a regression model without product term, in which the computational complexity is reduced. Secondly, by developing a moving window, the moving window output and moving window observation data are constructed, in which the estimation accuracy is lifted. Then, based on moving data, a filter is introduced to filter noise data, and to improve the bias estimation issue. Thirdly, by designing the forcing variables with adaptive attenuation coefficient, the estimation error data can be got which is used to develop estimator, in which it gives an optional scheme to design the adaptive estimator compared with the prediction error and observation error criterion. Finally, the example results demonstrate that the developed method is effective to achieve the parameter estimation for quantized nonlinear cascade system, and the has better performance compared with some estimators in term of estimation precision and convergence rate.