Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network |
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Authors: | M Reza Peyghami R Khanduzi |
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Affiliation: | (1) Department of Mathematics, K.N. Toosi University of Technology, P.O. Box 16315-1618, Tehran, Iran;(2) School of Mathematics, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran |
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Abstract: | In this paper, using the Hurst exponent value H, we first show that the automotive price in Iran Khodro Company (IRAN) is predictable and therefore a good forecasting can
be done using neural networks. We then introduce a new global and fast hybrid multilayer perceptron neural network (MLP-NN)
in order to forecast the automotive price. In our new framework, we hybridize the genetic algorithm (GA) and least square
(LS) method in order to train the connected weights of the network, which leads us to have a global and fast network. To do
so, the connected weights between input and hidden layers are trained by GA and the connected weights between the hidden and
output layers are trained by LS method. We finally apply our new MLP-NN to forecast the automotive price in Iran Khodro Company,
which is the biggest automotive manufacturing in IRAN. The results are well promising compared with the cases when we apply
the GA and LS individually. We also compare the results with the case when we employ the gradient-based optimization techniques
such as Levenberg–Marquardt method as well as some heuristic algorithms such as extended tabu search algorithm instead of
LS method and hybridization of MLP-LM with GA. |
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Keywords: | |
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