A conservative method of wavelet neural network based meta-modeling in constrained approximate optimization

Jongsoo Lee, Kwang Ho Shin

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The paper aims at the development of the wavelet neural network (WNN) based conservative meta-model that satisfies the constraint feasibility of approximate optimal solution. The WNN based constraint-feasible meta-model is formulated via exterior penalty method to optimally determine interconnection weights and dilation and translation coefficients in the network. Using Ackley's path function, the approximation performance of WNN is first tested in comparison with BPN. The proposed approach of constraint feasibility is then verified through a ten-bar planar truss problem. For constrained approximate optimization, the structural design of a composite rotor blade is explored to support the proposed strategies.

Original languageEnglish
Pages (from-to)109-126
Number of pages18
JournalComputers and Structures
Volume89
Issue number1-2
DOIs
Publication statusPublished - 2011 Jan 1

Fingerprint

Wavelet Neural Network
Metamodeling
Metamodel
Neural networks
Optimization
Structural Design
Penalty Method
Blade
Dilation
Structural design
Interconnection
Rotor
Turbomachine blades
Rotors
Optimal Solution
Composite
Path
Composite materials
Coefficient
Approximation

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Modelling and Simulation
  • Materials Science(all)
  • Mechanical Engineering
  • Computer Science Applications

Cite this

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A conservative method of wavelet neural network based meta-modeling in constrained approximate optimization. / Lee, Jongsoo; Shin, Kwang Ho.

In: Computers and Structures, Vol. 89, No. 1-2, 01.01.2011, p. 109-126.

Research output: Contribution to journalArticle

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