Abstract
Oil pumping equipment designers have to solve different types of optimization problems. Use of strong mathematical means is frequently very difficult, or, even impossible because of complexity of those problems. This paper suggests using genetic algorithms as an alternate facility to find optimal parameters of pumping unit under given particular conditions. Neural networks are employed to approximate the best solution using statistics on already found solutions for a set of conditions. Experimental results are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A.D. Bethke, “Genetic algorithms as function optimizers”, Ph.D. thesis, Dept. Computer and Communication Sciences, Univ. of Michigan, 1981.
L.B. Booker, “Intelligent behavior as an adaptation to the task of environment”, Ph.D. thesis, Dept. Computer and Communication Sciences, Univ. of Michigan, 1982.
D.J. Cavicchio, “Adaptive search using simulated evolution”, Ph.D. thesis, Dept. Computer and Communication Sciences, Univ. of Michigan, 1970.
K.A. DeJong, “Analysis of the behavior of a class of genetic adaptive systems”, Ph.D. thesis, Dept. Computer and Communication Sciences, Univ. of Michigan, 1975.
J.H. Holland, Adaptation in Natural and Artificial Systems, Univ. of Michigan, Ann Arbor, MI, 1975.
K. Hornik, M. Stinchcombe and H. White, “Multilayer feedforward networks are universal approximators”, Neural Networks, 2 (5), 1989, p. 359
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Berlin; New York: Springer-Verlag, 1992.
W.S. McCulloch, W. Pitts, “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biophysics, Vol. 9, 1943, p. 127.
M. Minsky, S. Papert, Perceptrons, Cambridge, Massachusetts: The MIT Press, 1969.
D.E. Rummelhart, G.E. Hinton, R.J. Williams, “Learning representations by back-propagating errors”, Nature 323, 1986, p. 533.
D.E. Rummelhart, G.E. Hinton, R.J. Williams, “Learning internal representations by back-propagating errors” In PDP Vol.1: Foundations, Ed D.E. Rummelhart, J.L. McClelland and The PDP Research Group, Cambridge, Massachusetts: The MIT Press, 1986, p. 318.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag/Wien
About this paper
Cite this paper
Vahidov, R.M., Vahidov, M.A., Eyvazova, Z.E. (1995). Use of Genetic and Neural Technologies in Oil Equipment Computer-Aided Design. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_83
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7535-4_83
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive