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Amir Hossein Darooneh

A. H. GHADERI and A. H. DAROONEH
ARTIFICIAL NEURAL NETWORK WITH REGULAR GRAPH FOR MAXIMUM AIR TEMPERATURE FORECASTING: THE EFFECT OF DECREASE IN NODES DEGREE ON LEARNING
Abstract


The behavior of nonlinear systems can be analyzed by artificial neural networks. Air tem- perature change is one example of the nonlinear systems. In this work, a new neural network method is proposed for forecasting maximum air temperature in two cities. In this method, the regular graph concept is used to construct some partially connected neural networks that have regular structures. The learning results of fully connected ANN and networks with proposed method are compared. In some case, the proposed method has the better result than con- ventional ANN. After specifying the best network, the effect of input pattern numbers on the prediction is studied and the results show that the increase of input patterns has a direct effect on the prediction accuracy.

 

 

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