Class Of Output Nonlinear Systems Research Articles

A recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input–output data filtering

Nonlinear systems exist widely in industrial processes. This paper studies the parameter estimation methods of establishing the mathematical models for a class of output nonlinear systems, whose output is nonlinear about the past outputs and linear about the inputs. We use an estimated noise transfer function to filter the input–output data and obtain two identification models, one containing the parameters of the system model, and the other containing the parameters of the noise model. Based on the data filtering technique, a data filtering based recursive least squares algorithm is proposed. The simulation results show that the proposed algorithm can generate more accurate parameter estimates than the recursive generalized least squares algorithm.

Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition

In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear ...

A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems

This paper proposes a generalized extended stochastic gradient (GESG) algorithm for estimating the parameters of a class of Wiener nonlinear autoregressive moving average systems using the gradient search. In order to improve the convergence rates of the GESG algorithm, a multi-innovation GESG algorithm is derived. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of a class of output nonlinear systems.

Newton iterative identification for a class of output nonlinear systems with moving average noises

This paper discusses iterative identification problems for a class of output nonlinear systems (i.e., Wiener nonlinear systems) with moving average noises from input–output measurement data, based on the Newton iterative method. The basic idea is to decompose a nonlinear system into two subsystems, to replace the unknown variables in the information vectors with their corresponding estimates at the previous iteration, and to present a Newton iterative identification method using the hierarchical identification principle. The numerical simulation results indicate that the proposed algorithms are effective.

Iterative identification algorithm for Wiener nonlinear systems using the Newton method

The Wiener system is an important class of output nonlinear systems. This paper presents a Newton iterative parameter estimation algorithm for Wiener nonlinear systems. The simulation results show that the proposed algorithm is effective. The proposed algorithm can be combined with other iterative methods to identify other linear or nonlinear systems with colored noises.

An iterative numerical algorithm for modeling a class of Wiener nonlinear systems

This letter presents an iterative estimation algorithm for modeling a class of output nonlinear systems. The basic idea is to derive an estimation model and to solve an optimization problem using the gradient search. The proposed iterative numerical algorithm can estimate the parameters of a class of Wiener nonlinear systems from input–output measurement data. The proposed algorithm has faster convergence rates compared with the stochastic gradient algorithm. The numerical simulation results indicate that the proposed algorithm works well.