The Application of Evolutionary Algorithms in the Artificial Neural Network Training Process for the Oilfield Equipment Malfunctions’ Forecasting

DOI

10.2991/3ca-13.2013.63How to use a DOI?

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

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - I.S. Korovin
AU  - M.V. Khisamutdinov
AU  - A.I. Kaliaev
PY  - 2013/04
DA  - 2013/04
TI  - The Application of Evolutionary Algorithms in the Artificial Neural Network Training Process for the Oilfield Equipment Malfunctions’ Forecasting
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation
PB  - Atlantis Press
SP  - 253
EP  - 257
SN  - 1951-6851
UR  - https://doi.org/10.2991/3ca-13.2013.63
DO  - 10.2991/3ca-13.2013.63
ID  - Korovin2013/04
ER  -