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

Authors:
Jongsoo Lee;Kwang Ho Shin
Affiliations:
School of Mechanical Engineering, Yonsei University, Seoul 120-749, Republic of Korea;School of Mechanical Engineering, Yonsei University, Seoul 120-749, Republic of Korea
Venue:
Computers and Structures
Year:
2011

Citing 2
Cited 0

Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

Neural Networks
Wavelet networks

IEEE Transactions on Neural Networks

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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.