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title:
 
The Application of Evolutionary Algorithms in the Artificial Neural Network Training Process for the Oilfield Equipment Malfunctions’ Forecasting
publication:
 
3ca-13
ISBN:
  978-90786-77-91-8
ISSN:
  1951-6851
DOI:
  doi:10.2991/3ca-13.2013.63 (how to use a DOI)
author(s):
 
I.S. Korovin, M.V. Khisamutdinov, Kaliaev A.I.
corresponding author:
 
I.S. Korovin
publication date:
 
November 2013
keywords:
 
neural network, genetic algorithm, oilfield equipment, forecasting, malfunction, mutation, crossingover
abstract:
 
The paper describes an evolutionary approach to artificial neural network (NN) training, which is used to determine the state of oil-production equipment. A new artificial NN weight coefficient coding method using multi-chromosomes is proposed. The genetic operators of crossingover and mutation applied to multi-chromosomes are examined. A genetic algorithm structure of artificial NN training based on the developed genetic operators is proposed. A comparison of the proposed approach to NN training with existing ones has been carried out.
copyright:
 
© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
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