Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-27T23:02:22.731Z Has data issue: false hasContentIssue false
  • English
  • Français

Simulating the behavior of poorly understood continua using neural networks

Published online by Cambridge University Press:  27 February 2009

Ian Flood
Ian Flood
Affiliation:
Department of Civil Engineering, University of Maryland, College Park, MD 20742, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper proposes and evaluates an artificial neural network based method of modeling the dynamic behavior of spatially distributed continuous engineering processes. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A description is first provided of a means of modeling the unknown component of governing differential equations. A range of levels of understanding of these equations is considered. A method of discretizing the resultant neural models of these equations is then illustrated, and the way in which these can be used to simulate the behavior of a process is described. The performance of the proposed neural network approach is then assessed in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The system is proven capable of providing highly accurate simulations of system behavior sustained over many thousands of simulation time steps. The paper concludes with an identification of several ongoing areas of further development and application of the proposed tool.

Keywords

ContinuaDiscretized ModelsDynamic ModelingPartial Differential EquationsSimulationNeural Networks
Type
Articles
Information
AI EDAM , Volume 10 , Issue 5 , November 1996 , pp. 391 - 400
Copyright
Copyright © Cambridge University Press 1996

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.)

References

REFERENCES

Ensley, D., & Nelson, D.E. (1992). Extrapolation of Mackey-Glass data using cascade correlation. Simulation 58(5), 333–339.CrossRefGoogle Scholar
Flood, I. (1991). A Gaussian-based feedforward network architecture and complementary training algorithm. Proc. Int. Joint Conf, on Neural Networks, IEEE and INNS, Vol.1, 171–176.Google Scholar
Flood, I., & Kartam, N. (1994). Neural networks in civil engineering. I:Principles and understanding. J. Comput. Civil Eng. ASCE 8(2), 131–148.CrossRefGoogle Scholar
Flood, I., & Worley, K. (1995). An artificial neural network approach to discrete-event simulation. AI EDAM 9, 37–49.Google Scholar
Gagarin, N., Flood, I., & Albrecht, P. (1992). Weighing trucks in motion using Gaussian-based neural networks. Proc. Int. Joint Conf. Neural Networks, (pp. 81–89). IEEE and INNS, Baltimore.Google Scholar
Gagarin, N., Flood, I., & Albrecht, P. (1994). Computing truck attributes with artificial neural networks. J. Comput. Civil Eng. ASCE 8(2), 179–200.CrossRefGoogle Scholar
Hecht-Nielsen, R. (1990). Neurocomputing. Addison-Wesley, Reading, MA.Google Scholar
Moody, J., & Darken, C.J. (1989). Fast learning in networks of locallytuned processing units. Neural Computation 1, 281–294.CrossRefGoogle Scholar
Shelton, R.O., & Peterson, J.K. (1992). Controlling a truck with an adaptive critic CMAC design. Simulation 58(5), 319–326.CrossRefGoogle Scholar