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.
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Abiyev, R.H. (2010). A Type-2 Fuzzy Wavelet Neural Network for Time Series Prediction. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_53
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DOI: https://doi.org/10.1007/978-3-642-13033-5_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13032-8
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