Title :
A Predictive Model of Nonlinear System Based on Generalized Regression Neural Network
Author :
Song, Yibin ; Ren, Ying
Author_Institution :
Sch. of Comput. Sci., Yantai Univ., Shandong
Abstract :
Generalized regression neural network (GRNN) is usually applied to the function approximation. Based on the principle of GRNN, this paper presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the training and predicting process of the nonlinear model. The simulations show the presented method has good effects on predicting the dynamic process of the nonlinear model, and could be applied on the prediction control for nonlinear systems satisfactorily
Keywords :
function approximation; large-scale systems; neurocontrollers; nonlinear control systems; predictive control; regression analysis; function approximation; generalized regression neural network; nonlinear complex system; prediction control; predictive model; Control system synthesis; Function approximation; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Power system modeling; Prediction algorithms; Predictive control; Predictive models;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1615018
