Hierarchical Newton Iterative Parameter Estimation of a Class of Input Nonlinear Systems Based on the Key Term Separation Principle

Abstract

This paper investigates the identification problem for a class of input nonlinear systems whose disturbance is in the form of the moving average model. In order to improve the computation complexity, the key term separation principle is introduced to avoid the redundant parameter estimation. Based on the decomposition technique, a hierarchical Newton iterative identification method combining the key term separation principle is proposed for enhancing the estimation accuracy and handling the computational load with the presence of the high dimensional matrices. In the identification procedure, the unknown internal items or vectors are replaced with their iterative estimates. The effectiveness of the proposed identification methods is shown via a numerical simulation example.

Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems

“…Parameter estimation of multivariable systems has attracted extensive research attention over the past decades, and many different identification approaches have been proposed to solve the parameter identification problems of multivariable systems, such as the hierarchical identification principle and the coupling identification concept [46,47]. The core idea of the hierarchical identification principle is to decompose the original model into several submodels, and to combine other approaches to estimate the parameters of the submodels [48,49]. The coupled identification methods have been derived to identify the parameters of multivariable systems and were first presented in [50].…”