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Structural Engineering and Mechanics Volume 6, Number 8, December 1998 , pages 955-969 DOI: https://doi.org/10.12989/sem.1998.6.8.955 |
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Soft computing with neural networks for engineering applications: Fundamental issues and adaptive approaches |
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Jamshid Ghaboussi and Xiping Wu
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| Abstract | ||
| Engineering problems are inherently imprecision tolerant. Biologically inspired soft computing methods are emerging as ideal tools for constructing intelligent engineering systems which employ approximate reasoning and exhibit imprecision tolerance. They also offer built-in mechanisms for dealing with uncertainty. The fundamental issues associated with engineering applications of the emerging soft computing methods are discussed, with emphasis on neural networks. A formalism for neural network representation is presented and recent developments on adaptive modeling of neural networks, specifically nested adaptive neural networks for constitutive modeling are discussed. | ||
| Key Words | ||
| neural networks, soft computing, computational mechanics, constitutive models. | ||
| Address | ||
| Ghaboussi J, Univ Illinois, Dept Civil Engn, Urbana, IL 61801 USA Univ Illinois, Dept Civil Engn, Urbana, IL 61801 USA Exxon Prod Res Co, Offshore Div, Houston, TX 77252 USA | ||