DocumentCode
322669
Title
Recurrent NN model for chaotic time series prediction
Author
Zhang, Jun ; Tang, K.S. ; Man, K.F.
Author_Institution
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong
Volume
3
fYear
1997
fDate
9-14 Nov 1997
Firstpage
1108
Abstract
A new Elman neural network learning algorithm is proposed for chaotic time series prediction. This method has a number of advantages over the use of a standard backpropagation algorithm. It is not only its capability for handling a much higher complexity time data series, but its superiority in time convergence can prove to be a valuable asset for time critical applications. Furthermore, this method is also very accurate in prediction as it can reach global minimum in a much attainable manner
Keywords
chaos; learning (artificial intelligence); recurrent neural nets; time series; Elman neural network learning algorithm; chaotic time series prediction; global minimum; recurrent neural networks; time convergence; time data series; Artificial neural networks; Chaos; Computer networks; Convergence; Electronic mail; Feedforward systems; Neural networks; Predictive models; Recurrent neural networks; Time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics, Control and Instrumentation, 1997. IECON 97. 23rd International Conference on
Conference_Location
New Orleans, LA
Print_ISBN
0-7803-3932-0
Type
conf
DOI
10.1109/IECON.1997.668440
Filename
668440
Link To Document