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Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction. (Abiyev, Rahib H.,)

Bibliographical information (record 263976)

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction.
Author:	Abiyev, Rahib H., Search Author in Amazon Books

Edition:	2011.
Classification:	HG3810
URL:	http://library.neu.edu.tr:2048/login?url=http://dx.doi.org/10.1007/s00521-010-0414-4

Detailed notes

- This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models. [ABSTRACT FROM AUTHOR] . Copyright of Neural Computing & Applications is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.