Title :
Research on an Improved GMDH-type Neural Network Based on Reconstruction of Phase Space
Author :
Zhang, Dahai ; Chen, Qijuan ; Jiang, Sheng ; Xi, Bo
Author_Institution :
Coll. of Power & Mech. Eng., Wuhan Univ.
Abstract :
An improved GMDH-type neural network based on the reconstruction of phase space and its application to complex systems are proposed. The structure of the conventional GMDH (group method of data handling) neural network is fixed, and it is difficult to obtain the suitable threshold value and the output of the network is best in local. The new model of the GMDH-type neural network is improved in two ways: one is that GA and selection coefficients are adopted to get the best structure of the network and the best solution; the other is that the mean of the fitness function is used as the threshold value, in this way the necessary data are reserved and the needless terms are adequately omitted. The improved GMDH-type neural network proposed is fit for the prediction of chaotic time series and the result of the simulation demonstrates that the new network makes better performance than the conventional one
Keywords :
control engineering computing; forecasting theory; genetic algorithms; identification; large-scale systems; neural nets; phase space methods; chaotic time series prediction; complex system; fitness function; genetic algorithms; group method of data handling-type neural network; phase space reconstruction; Chaos; Data handling; Delay; Educational institutions; Electronic mail; Genetic algorithms; Intelligent control; Mechanical engineering; Neural networks; Predictive models; GMDH neural network; genetic algorithms; the reconstruction of phase space;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712629
