An evaluation of back-propagation neural networks for the optimal design of structural systems. I: Training procedures
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Publication:1600798
DOI10.1016/S0045-7825(01)00372-3zbMath1131.74332MaRDI QIDQ1600798
Publication date: 16 June 2002
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Neural networks for/in biological studies, artificial life and related topics (92B20) Optimization problems in solid mechanics (74P99)
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Uses Software
Cites Work
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- Two-point constraint approximation in structural optimization
- Design sensitivity analysis of structural systems
- Nonlinear programming codes. Information, tests, performance
- Multilayer feedforward networks are universal approximators
- Structural optimization using evolution strategies and neural networks
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- On some experiments which delimit the utility of nonlinear programming methods for engineering design
- Neurobiological computational models in structural analysis and design
- Counterpropagation neural networks in decomposition based optimal design
- A hybrid numerical/neurocomputing strategy for sensitivity analysis of nonlinear structures
- Optimum design of aerospace structural components using neural networks
- Neural Network Approximator with Novel Learning Scheme for Design Optimization with Variable Complexity Data
- A neural dynamics model for structural optimization—Theory
- A neural dynamics model for structural optimization—Application to plastic design of structures
- Neural networks and physical systems with emergent collective computational abilities.
- Approximation by superpositions of a sigmoidal function
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