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. © 2010 Springer-Verlag.
CITATION STYLE
Abiyev, R. H. (2010). A type-2 fuzzy wavelet neural network for time series prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6098 LNAI, pp. 518–527). https://doi.org/10.1007/978-3-642-13033-5_53
Mendeley helps you to discover research relevant for your work.