Release Time:2022-06-27  Hits:

Date of Publication: 2011-01-01

Journal: 大连海事大学学报

Volume: 37

Issue: 2

Page Number: 101-105

ISSN: 1006-7736

Abstract: To solve the problems about nonlinear systems of model identification
   and predictive control in the industrial process control,a strategy of
   model predictive control based on BP neural network was presented which
   adopted the method to identify Hammerstein-Wiener model coefficient
   based on least squares support vector machines and then to build
   nonlinear predictive controller.A nonlinear predictive control algorithm
   based on least support vector machines Hammerstein-Wiener model was
   realized by using BP neural network to train predictive input sequences
   and using Quasi-Newton method to solve nonlinear predictive control
   rule.Simulation result illustrates that the proposed approach is
   effective and feasible.

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