Recurrent NN model for chaotic time series prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1108-1112
Journal / PublicationIECON Proceedings (Industrial Electronics Conference)
Publication statusPublished - 1997


TitleProceedings of the 1997 23rd Annual International Conference on Industrial Electronics, Control, and Instrumentation, IECON. Part 2 (of 4)
CityNew Orleans, LA, USA
Period9 - 14 November 1997


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 Back-Propagation (BP) 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 application. Furthermore, this method is also very accurate in prediction as it can reach global minimum in a much attainable manner.