A type-2 fuzzy wavelet neural network for time series prediction

Authors:
Rahib H. Abiyev
Affiliations:
Department of Computer Engineering, Near East University, North Cyprus, Turkey
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Year:
2010

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Abstract

This paper presents the development of novel type-2 wavelet neural network system for time series prediction. The structure of type-2 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 fuzzy rules that includes type-2 fuzzy sets in the antecedent part and a wavelet function in the consequent part of the rules. For generating the structure of prediction model a fuzzy clustering algorithm is implemented to generate the rules automatically and the gradient learning algorithm is used for parameter identification. Type-2 FWNN is used for modelling and prediction of exchange rate time series. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of type-2 FWNN based systems and with the comparative simulation results of previous related models.