Title: Application of multi-layer recurrent neural network in chaotic time series prediction: a real case study of crude oil distillation capacity
Authors: Kaveh Khalili-Damghani; Soheil Sadi-Nezhad
Addresses: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778, Iran. ' Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778, Iran
Abstract: A full customised case-oriented Multi-Layered Recurrent Neural Network (MLRNN) has been proposed to predict the Capacity of Crude Oil Distillation in OPEC Member Countries. Recurrent neural networks use feedback connections and have the potential to represent certain computational structures in a more parsimonious fashion. Moreover, a cluster based training procedure, in which proper opportunity achieves for network to sense complicated nonlinear relations in data, has been supplied. The results of proposed MLRNN were promising in comparison with the results of a Multi-Layered Feed-Forward Neural Network (MLFFNN) on the aforementioned case study.
Keywords: recurrent neural networks; time series prediction; crude oil distillation; distillation capacity prediction; OPEC.
International Journal of Artificial Intelligence and Soft Computing, 2011 Vol.2 No.4, pp.367 - 380
Available online: 27 Sep 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article