Neural Network Nonlinear Predictive Control Based on Tent-map Chaos Optimization

摘要

With the unique ergodicity,irregularity,and special ability to avoid being trapped in local optima,chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields,such as nonlinear programming problems.In this article,a novel neural network nonlinear predic- tive control(NNPC)strategy based on the new Tent-map chaos optimization algorithm(TCOA)is presented.The feedforward neural network is used as the multi-step predictive model.In addition,the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC.Simulation on a labora- tory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.