Applying least squares support vector machines to the airframe wing-box structural design cost estimation

Authors:
S. Deng;Tsung-Han Yeh
Affiliations:
Department of Power Vehicle and Systems Engineering, Chung-Cheng Institute of Technology, National Defense University, No. 190, Sanyuan 1st St., Dasi Township, Taoyuan Country 33509, Taiwan, ROC;School of National Defense Science, Chung-Cheng Institute of Technology, National Defense University, No. 190, Sanyuan 1st St., Dasi Township, Taoyuan Country 33509, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 7
Cited 1

Practical methods of optimization; (2nd ed.)

Practical methods of optimization; (2nd ed.)
The nature of statistical learning theory

The nature of statistical learning theory
A neural network approach for early cost estimation of packaging products

Computers and Industrial Engineering
Decision support with neural networks in the management of research and development: concepts and application to cost estimation

Information and Management
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Predicting corporate financial distress based on integration of support vector machine and logistic regression

Expert Systems with Applications: An International Journal

A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Robotics and Computer-Integrated Manufacturing

Quantified Score

Hi-index	12.05

Visualization

Abstract

This research used the least squares support vector machines (LS-SVM) method to estimate the project design cost of an airframe wing-box structure. We also compared the estimation performance using back-propagation neural networks (BPN) and statistical response surface methodology (RSM). The solution mechanism of the LS-SVM involved a simultaneous searched for the maximal margin as the target, taking into account the error calculated during training phase to determine the estimation problem models. Two case studies involving the wing-box structure was investigated the separate structural parts case and the mixed structural parts case. The test results verified the feasibility of using the LS-SVM as well as its ability to make accurate estimations.