An evaluation of back-propagation neural networks for the optimal design of structural systems. II: Numerical evaluation

DOI10.1016/S0045-7825(02)00213-XzbMath1131.74333MaRDI QIDQ1600799

Ganesh Subbarayan, Li Zhang

Publication date: 16 June 2002

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

Structural optimizationApproximation methodsFeed forward neural networksTraining algorithms

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Optimization problems in solid mechanics (74P99)

Related Items (7)

Multi-objective meta level soft computing-based evolutionary structural design ⋮ How to control the crack to propagate along the specified path feasibly? ⋮ Bayesian regularization neural networks for optimizing fluid flow processes ⋮ Subproblem optimization with regression and neural network approximators ⋮ An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization ⋮ A cascade optimization methodology for automatic parameter identification and shape/process optimization in metal forming simulation ⋮ A hybrid artificial neural network method with uniform design for structural optimization

Uses Software

• HYBRJ
• minpack
• NLPQL

Cites Work

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• Design sensitivity analysis of structural systems
• Nonlinear programming codes. Information, tests, performance
• Test examples for nonlinear programming codes
• A study of mathematical programmingmethods for structural optimization. Part II: Numerical results

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