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

Jongsoo Lee; Kwang Ho Shin

doi:10.1016/j.compstruc.2010.08.007

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

Jongsoo Lee, Kwang Ho Shin

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

8 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 language	English
Pages (from-to)	109-126
Number of pages	18
Journal	Computers and Structures
Volume	89
Issue number	1-2
DOIs	https://doi.org/10.1016/j.compstruc.2010.08.007
Publication status	Published - 2011 Jan

All Science Journal Classification (ASJC) codes

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

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10.1016/j.compstruc.2010.08.007

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

Abstract

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