Elsevier

Information Sciences

Volume 112, Issues 1–4, December 1998, Pages 125-136
Information Sciences

Predicting a chaotic time series using a fuzzy neural network

https://doi.org/10.1016/S0020-0255(98)10026-9Get rights and content

Abstract

In this paper the authors present an alternative neurofuzzy architecture for application to chaotic time series prediction. The architecture employs an approximation to the fuzzy reasoning system to considerably reduce the dimensions of the network as compared to similar approaches. The application considered is the chaotic Mackey-Glass differential equation. Simulation results for single and multi-step predictions were obtained using the MATLAB neural network toolbox and these are compared with both traditional neural network implementations and other fuzzy reasoning approaches. The work not only demonstrates the advantage of the neurofuzzy approach but it also highlights the advantages of the architecture for hardware realisations.

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