Modeling method for nonlinear system for stable parameter estimation based on RBF-ARX model

Abstract

The invention discloses a modeling method for a nonlinear system for stable parameter estimation based on a RBF-ARX model. A to-be-estimated model parameter is classified into a linear parameter and a nonlinear parameter for separately optimizing and an offline parameter estimation method for ensuring the model parameter stability by fusing regularization technique with nonlinear least square method is designed, in order to solve the problems that the nonlinear dynamic and physical model is difficult to acquire in the practical industrial process, the RBF-ARX model is adopted for describing the dynamic characteristics of the object and the problem of oversize or unstable model parameter is possibly caused by more and potential morbidity of the model parameter. Compared with the prior art, the modeling method provided by the invention has the advantages that long-term forecast accuracy and robustness of the RBF-ARX model can be greatly increased; the method is fit for all the parameter optimizing problems of the nonlinear model for classifying the parameter into a linear parameter part and a nonlinear parameter part; the modeling method has ultrahigh practical value for the problem of engineering design and optimizing based on the computer numerical simulation analysis.

Description

technical field [0001] The invention relates to the field of engineering design and optimization, and relates to a nonlinear system modeling method which can ensure the stable parameter estimation of the RBF-ARX model and improve the long-term prediction accuracy and robustness of the RBF-ARX model. Background technique [0002] In the actual industrial process, nonlinear dynamic and physical models are generally difficult to obtain, and the use of data-driven modeling methods to describe its characteristics is the premise of its analysis and control. RBF neural network is widely used in pattern recognition, function approximation, signal processing, nonlinear system modeling and other fields because of its simple structure, high nonlinear approximation accuracy and fast learning ability, and has become the most popular front-end neural network. One of the feed neural networks. However, due to the complexity of the actual industrial process objects, the RBF neural network u...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an RBF-ARX model based on the problem of the long-term prediction performance and robustness of the model caused by the divergence of model estimation parameters that may exist in the background technology for nonlinear system modeling. Nonlinear System Modeling Method for Stable Parameter Estimation

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