Abstract
The model identification of the nonlinear system has been concerned by the industrial community all along. The relationship of the nonlinear dynamic system is contained in the data accumulated in the scene. To better utilize the data about the industrial objects, in this article, we put forward the nonlinear system predictor driven by the Bayesian-Gaussian neural network (NN) model, use the trained threshold matrix and sliding window data to realize the online output prediction for the nonlinear dynamic system. The simulation experiment indicates that the Bayesian-Gaussian NN based on the sliding window data can fulfill the demands of the online identification and prediction of the adaptive nonlinear system.
Highlights
The model identification of the nonlinear system has been concerned by the industrial community all along
Y(k) denotes the output of the k’th step of the system, y(k − i) (i=1, 2... n) denotes the system output of the former n steps, u(k) denotes the input of the k’th step of the system, u(k − i) (i=1, 2... m) denotes the system control inputs of the former m steps, f denotes the dynamic relationship between input and output of the dynamic system, and the nonlinear function relationship can be approximated by the identification method, and the target of the article is to use the Bayesian-Gaussian neural network (NN) model based on sliding data window to approximate the structure of the nonlinear function f and the online identification and prediction of the dynamic system
And the dimension of the threshold matrix is equal to the input amount of the nonlinear dynamic system, so the parameters which need to be confirmed from the network are few, and the operation time of the reasoning model can be largely saved
Summary
Y(k) denotes the output of the k’th step of the system, y(k − i) N) denotes the system output of the former n steps, u(k) denotes the input of the k’th step of the system, u(k − i) M) denotes the system control inputs of the former m steps, f denotes the dynamic relationship between input and output of the dynamic system, and the nonlinear function relationship can be approximated by the identification method, and the target of the article is to use the Bayesian-Gaussian NN model based on sliding data window to approximate the structure of the nonlinear function f and the online identification and prediction of the dynamic system Y(k) denotes the output of the k’th step of the system, y(k − i) (i=1, 2... n) denotes the system output of the former n steps, u(k) denotes the input of the k’th step of the system, u(k − i) (i=1, 2... m) denotes the system control inputs of the former m steps, f denotes the dynamic relationship between input and output of the dynamic system, and the nonlinear function relationship can be approximated by the identification method, and the target of the article is to use the Bayesian-Gaussian NN model based on sliding data window to approximate the structure of the nonlinear function f and the online identification and prediction of the dynamic system
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