Method to determine initial value of local optimization for neural network predictive control
FAN Zhao-feng
MA Xiao-ping
SHAO Xiao-gen
Abstract：To deal with the performance degradation caused by improper initial values in neural network local optimiza-tion predictive control, we propose a method to dynamically determine the initial values. In each optimization cycle the minimum output error point is selected by calculating the inverse neural network. The existence of the minimal value of the objective function between this point and the current control point can be ensured and proved through modifying the weighting factor. Finally, a simulation experiment is carried out to verify the proposed method using a back propagation (BP) neural network as the predictive model, and the Newton-Raphson algorithm is employed as the receding horizon op-timization strategy. The results show that the initial value problem can be solved to improve the reliability of the control system.
Keywords：neural networksmodel predictive controloptimizationinitial value problems
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 741-747 )