A nonlinear predictive model based on BP neural network

Author

Li, Huijun

Author_Institution

Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China

fYear

2010

fDate

26-28 May 2010

Firstpage

73

Lastpage

77

Abstract

MPCs have been widely applied in industrial process control field because of the excellent control effect. The classic MPCs, which are all based on linear predictive models, are unfit for the strong-nonlinearity control systems. In these cases, NMPCs must be constructed if a model predictive controller wants to be used. Nonlinear predictive model is the foundation of NMPC, and should be established firstly. This paper proposed a one-step nonlinear predictive model based on BP neural network by combining NARMAX model and neural network, and supplied a calculation method of the hidden-layer-neuron number of the two-layer BP neural network used in the one-step predictive model.

Keywords

autoregressive moving average processes; backpropagation; neural nets; nonlinear control systems; predictive control; BP neural network; NARMAX model; backpropagation; hidden-layer-neuron number; linear predictive models; nonlinear autoregressive moving average with exogenous inputs; nonlinear predictive model; one-step predictive model; strong-nonlinearity control systems; Artificial neural networks; Autoregressive processes; Control system synthesis; Electrical equipment industry; Industrial control; Mathematical model; Neural networks; Nonlinear systems; Predictive control; Predictive models; BP Neural Network; MPC; NARMAX; Predictive Model;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5499123

Filename

5499123