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Notice of Violation of IEEE Publication Principles
A hybrid particle swarm optimization–neural network strategy for permeability estimation of the reservoir

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3 Author(s)
Nasimi, R. ; Dept. of Autom. & Instrum., Pet. Univ. of Technol., Ahwaz, Iran ; Shahbazian, M. ; Irani, R.

Notice of Violation of IEEE Publication Principles

"A Hybrid Particle Swarm Optimization-Neural Network Strategy for Permeability Estimation of the Reservoir"
by Reza Nasimi, Mehdi Shahbazian, Rasoul Irani
in the 2010 International Symposium on Power Electronics Electrical Drives Automation and Motion (SPEEDAM), 2010, pp. 697 - 702

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training"
by Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok, Michael R. Lyu
in the Applied Mathematics and Computation 185, 2007, pp. 1026 - 1037

In this work we investigate how the integration of back propagation with particle swarm optimization (PSO) improves the reliability and prediction capability of artificial neural networks. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid particle swarm optimization-back propagation strategy (PSO-BP). The particle swarm optimization algorithm has been proved to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, with higher- accuracy. PSO is used to decide the initial weights of the gradient decent methods intelligently. The experimental results show that the proposed hybrid PSO- BP algorithm reveals better performance than the conventional BP algorithm for permeability estimation.

Published in:

Power Electronics Electrical Drives Automation and Motion (SPEEDAM), 2010 International Symposium on

Date of Conference:

14-16 June 2010