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NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches

Gao, Y and Er, MJ (2005) NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches FUZZY SET SYST, 150 (2). pp. 331-350.

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Abstract

The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its capability of accommodating the dynamic, complex and nonlinear nature of real-world time series prediction problems. This paper focuses on the modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN) methodology. Both feedforward and recurrent FNNs approaches are proposed. Furthermore, an efficient algorithm for model structure determination and parameter identification with the aim of producing improved predictive performance for NARMAX time-series models is developed. Experiments and comparative studies demonstrate that the proposed FNN approaches can effectively learn complex temporal sequences in an adaptive way and they outperform some well-known existing methods.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Surrey Space Centre
Authors :
AuthorsEmailORCID
Gao, YUNSPECIFIEDUNSPECIFIED
Er, MJUNSPECIFIEDUNSPECIFIED
Date : 1 March 2005
Identification Number : https://doi.org/10.1016/j.fss.2004.09.015
Uncontrolled Keywords : time series prediction, fuzzy neural networks, Takagi-Sugeno-Kang fuzzy inference systems, NARMAX models, INFERENCE SYSTEM, IDENTIFICATION, ALGORITHM
Additional Information : Copyright © 2004 Elsevier B.V. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Fuzzy Sets and Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Fuzzy Sets and Systems, 150(2), March 2005, DOI:10.1016/j.fss.2004.09.015
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:38
Last Modified : 28 Mar 2017 14:38
URI: http://epubs.surrey.ac.uk/id/eprint/199697

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