Integration of Som Network and Evolutionary Algorithms to Train Rbf Network for Forecasting
نویسندگان
چکیده
This paper intends to propose an integrated method which combines selforganizing map (SOM) network with genetic algorithm (GA) and particle swarm optimization (PSO)-based (ISGP) algorithm to train the radial basis function (RBF) network for function approximation. The experimental results for three benchmark problems indicated that such integration can have better performance. In addition, using the proposed ISGP algorithm to exercise oil price forecasting also showed that the proposed algorithm is able to achieve more promising accuracy than the auto-regressive integrated moving average (ARIMA) model and four evolutionary algorithms (EAs) proposed in literatures.
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