A COMPARISON OF POLYNOMIAL APPROXIMATIONS AND ARTIFICIAL NEURAL NETS AS RESPONSE SURFACES

被引:46
作者
CARPENTER, WC
BARTHELEMY, JFM
机构
[1] Dept. of Civil Engineering and Mechanics, University of South Florida, Tampa, 33620, FL
[2] NASA Langley Research Center, MS246, Hampton, 23681-0001, VA
来源
STRUCTURAL OPTIMIZATION | 1993年 / 5卷 / 03期
关键词
D O I
10.1007/BF01743353
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Abstract Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net and the number of designs needed to train an approximation is discussed.
引用
收藏
页码:166 / 174
页数:9
相关论文
共 13 条
[1]  
Anderson J., Rosenfeld E., Neurocomputing
[2]  
foundations of research, (1988)
[3]  
Berke L., Hajela P., Application of artificial neural nets in structural mechanics, Shape and layout optimization of structural systems. (CISM lecture series, Udine, Italy, 1990), (1992)
[4]  
Box G.E.P., Draper N.R., Empirical model-building and response surfaces, (1987)
[5]  
Brown R.T., Nachlas J.A., Structural optimization of laminated conical shells, AIAA J., 23, pp. 781-787, (1985)
[6]  
Carpenter W.C., Smith E.A., Computional efficiency of nonlinear programming methods on a class of structional problems, Int. J. Num. Meth. Eng., 1, pp. 1203-1223, (1977)
[7]  
Fleury C., First and second order convex approximation strategies in structural optimization, Struct. Optim., 1, pp. 3-10, (1989)
[8]  
Fox R.L., Optimization methods for engineering design, (1971)
[9]  
Hornik K., Stinchombe M., White H., Multilayer feedforward networks are universal approximators, Neural Networks, 2, pp. 359-366, (1989)
[10]  
Reklaitis G.V., Ravindran A., Ragsdell K.M., Engineering optimization, methods and applications, (1983)