Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-15T14:08:27.485Z Has data issue: false hasContentIssue false
  • English
  • Français

Guidelines for the selection of network architecture

Published online by Cambridge University Press:  27 February 2009

William C. Carpenter and
Margery E. Hoffman
William C. Carpenter
Affiliation:
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, U.S.A.
Margery E. Hoffman
Affiliation:
Naval Air Warfare Center, Aircraft Division, Patuxent River, MD 20670, U.S.A.
Get accessRights & Permissions [Opens in a new window]

Abstract

This paper is concerned with presenting guidelines to aide in the selection of the appropriate network architecture for back-propagation neural networks used as approximators. In particular, its goal is to indicate under what circumstances neural networks should have two hidden layers and under what circumstances they should have one hidden layer. Networks with one and with two hidden layers were used to approximate numerous test functions. Guidelines were developed from the results of these investigations.

Keywords

ApproximationsApproximatorsNeural Networks

Information

Type
Articles
Information
AI EDAM , Volume 11 , Issue 5 , November 1997, pp. 395 - 408
Copyright
Copyright © Cambridge University Press 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Anderson, J., & Rosenfeld, E. (1988). Neurocomputing: Foundations of Research. MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Anderson, D., Himes, E.L., Authur, S.J., & Eiap, E.L. (1993). Application of artificial neural networks to prediction of minor axis steel connections. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Amirikian, B., & Nishimura, H. (1994). What size network is good for generalization of a specific task of interest? Neural Networks 7(2), 321–329.CrossRefGoogle Scholar
Bartlett, E.B. (1994). Dynamic node architecture learning: An information theoretic approach. Neural Networks 7(1), 129–140.CrossRefGoogle Scholar
Box, G.E.P., & Draper, N.R. (1987). Empirical Model-Building and Response Surfaces. John Wiley & Sons, New York.Google Scholar
Carpenter, W.C. (1993). Effect of design selection on response surface performance. Final Report, NASA Grant No. NAG-1–1378.Google Scholar
Carpenter, W.C., & Barthelemy, J.-F.M. (1993). A comparison of polynomial approximations and artificial neural nets as response surfaces. Structural Optimization 5, 1–15.CrossRefGoogle Scholar
Carpenter, W.C., & Hoffman, M.E. (1995). Training backprop neural networks. A.I. Expert, March, 30–33.Google Scholar
Carpenter, W.C., & Hoffman, M.E. (1997). Selecting the architecture of a class of back-propagation neural networks used as approximators. Artificial Intelligence in Engineering 11, 33–44.Google Scholar
Demuth, H., & Beale, M. (1994). Neural Network Toolbox for Use with MATLAB. The Math Works, Natick, MA.Google Scholar
Fujita, O. (1992). Optimization of the hidden unit function in feedforward neural networks. Neural Networks 5, 755–764.CrossRefGoogle Scholar
Hajela, P., & Berke, L. (1990). Neurobiological computational models in structural analysis and design. Paper AIAAA-90–1133-Cp. AIAA/ASME/ASCE/AHS/ASC 31st Structures, Structural Dynamics and Materials Conference, New York.CrossRefGoogle Scholar
Hoffman, M. (1993). An investigation of artificial neural networks for F-14A wing-sweep angle calculations. Report No. NAWCADWAR-93028–60, Naval Air Warfare Center, Aircraft Division, Warminster, PA.Google Scholar
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366.Google Scholar
Kahkonen, K., & Pallas, J. (1993). Roles, benefits and objectives of neural networks in building construction practice. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Kirkegaard, P.H., & Rytter, A. (1993). The use of neural networks for damage detection and location in a steel member. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Khuri, A.I., & Cornell, J.A. (1987). Response Surfaces, Designs and Analyses. Marcel Dekker, New York.Google Scholar
Liong, S.Y., & Chan, W.T. (1993). Runoff volume estimates with neural networks. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Lippmann, R.P. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine 4(2), 4–22.CrossRefGoogle Scholar
Lu, P.-C, & Urquidi-MacDonald, M. (1994). Prediction of IGSCC in TYPE 304 SS using an artificial neural network. Paper 151. Corrosion94, The Annual Conference and Corrosion Show, NACE International, Houston, TX, 151/1–151/20.Google Scholar
Lyons, G., & Hunt, J. (1993). Traffic models—A role for neural networks? In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Mawdesley, M.J., & Carr, V. (1993). Artificial neural networks for construction project planning. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Myers, R.H. (1971). Response Surface Methodology. Allyn and Bacon, Boston, MA.Google Scholar
Rogers, K.L. (1994). Simulating structural analysis with neural nets. Journal of Computing in Civil Engineering 8(2), 252–265.CrossRefGoogle Scholar
Rumelhart, D., & McClelland, J. (1986). Parallel Distributed Processing, 2 vols. MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Swift, R.A., & Batill, S.M. (1991). Application of neural networks to preliminary structural design. Paper AIAA/ASME/AHS/ASC. 32nd Structures, Structural Dynamics and Materials Conference, Baltimore, MD.CrossRefGoogle Scholar
Wang, Z., Tham, M., & Morris, A.J. (1992). Multilayer feedforward neural networks: A canonical form approximation of nonlinearity. International Journal of Control 56, 655–672.CrossRefGoogle Scholar
Wang, Z., Di Massimo, C., Tham, M.T., & Morris, A.J. (1994). A procedure for determining the topology of multilayer feedforward neural networks. Neural Networks 7(2), 291–300.CrossRefGoogle Scholar
Williams, T.P. (1993). Neural networks to predict construction cost indexes. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar
Wu, X., & Lim, S.Y. (1993). Prediction of maximum scour depth at spur dikes with adaptive neural networks. In Neural Networks and Combinatorial Optimization in Civil and Structural Engineering. Civil-Comp Press Limited, Edinburgh, UK.Google Scholar