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Proceedings Paper

Solving nonlinear engineering problems with the aid of neural networks
Author(s): Andrew H. Sung; Hujun J. Li; Shih-Hsien Chang; Reid Grigg
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Paper Abstract

In this paper, a technique is presented for using neural networks as an aid for solving nonlinear engineering problems, which are encountered in optimization, simulations and modeling, or complex engineering calculations. Iterative algorithms are often used to find the solutions of such problems. For many large-scale engineering problems, finding good starting points for the iterative algorithms is the key to good performance. We describe using neural networks to select starting points for the iterative algorithms for nonlinear systems. Since input/output training data are often easily obtained from the problem description or from the system equations, a neural network can be trained to serve as a rough model of the underlying problem. After the neural network is trained, it is used to select starting points for the iterative algorithms. We illustrate the method with four small nonlinear equation groups, two real applications in petroleum engineering are also given to demonstrate the method's potential application in engineering.

Paper Details

Date Published: 1 November 1999
PDF: 11 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367695
Show Author Affiliations
Andrew H. Sung, New Mexico Institute of Mining and Technology (United States)
Hujun J. Li, New Mexico Institute of Mining and Technology (United States)
Shih-Hsien Chang, New Mexico Institute of Mining and Technology (United States)
Reid Grigg, New Mexico Institute of Mining and Technology (United States)


Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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