NONLINEAR TIME SERIES FORECASTING USING RADIAL BASIS FUNCTION NETWORK-SELF ORGANIZING MAP (RBFN-SOM)

ABSTRACT. In recent years, Artificial Neural Network (ANN) has ben proposed as a promising alternative approach to nonlinear time series modeling and forecasting. Radial Basis Function Network (RBFN) is an ANN which has been widely used for forecasting. In the confenssional RBFN, the number of inputs is equal to the number of centers as a parameters of RBFN. The architecture of RBFN will be ineffective if the number of inputs are large. Self Organizing Map is the proposed algorithm to reduce parameters by clustering the centers. Then, modified of RBFN is called by RBFN-SOM. In this study, RBFN-SOM algorithm is more emphasized on the construction of the algorithm in Matlab programming. The result program can be used to forecast the data in the next period.

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