Use of Genetic and Neural Technologies in Oil Equipment Computer-Aided Design
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.
KeywordsGenetic Algorithm Weight Coefficient Predetermined Interval Pumping Unit Universal Approximators
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- A.D. Bethke, “Genetic algorithms as function optimizers”, Ph.D. thesis, Dept. Computer and Communication Sciences, Univ. of Michigan, 1981.Google Scholar
- 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.Google Scholar
- D.J. Cavicchio, “Adaptive search using simulated evolution”, Ph.D. thesis, Dept. Computer and Communication Sciences, Univ. of Michigan, 1970.Google Scholar
- 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.Google Scholar
- J.H. Holland, Adaptation in Natural and Artificial Systems, Univ. of Michigan, Ann Arbor, MI, 1975.Google Scholar
- W.S. McCulloch, W. Pitts, “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biophysics, Vol. 9, 1943, p. 127.Google Scholar
- 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.Google Scholar