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Using Artificial Neural Networks to Model Non-Linearity in a Complex System

  • P. Weller
  • A. Thompson
  • R. Summers
Conference paper

Abstract

This paper describes an investigation into using artificial neural networks (ANNs) to model the non-linearities in a complex system, a nuclear reactor. A simple one compartment finite difference model of the plant is developed and an exact ANN equivalent formed directly without training. Conventional training using standard transfer functions available in ANN packages is compared with this. A novel method is used to produce the ANN training and test sets. A twenty five compartment model is built from directly formed ANNs and the results compared to a simulator model.

Keywords

Artificial Neural Network Artificial Neural Network Model Compartment Model Steam Generator Reactor Pressure Vessel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    A. G. Parlos, K. T. Chong, and A. F. Atiya. Empirical model development and validation with dynamic learning in the recurrent multilayer perception. Nuclear Technology, 106:271–290, 1994.Google Scholar
  2. [2]
    A. C. Thompson, P. R. Weiler, and R. Summers. The use of neural networks in a system transient code. In Proceedings of ASME & JSME Fluid Engineering Symposium on Validation of System Transient Codes, pages 168–173, 1995.Google Scholar
  3. [3]
    V. R. Vemuri and R. D. Rogers, editors. Artificial Neural Networks: Forecasting Time Series. IEEE Computer Society Press, 1994.Google Scholar
  4. [4]
    P. R. Weiler, R. Summers, and A. C. Thompson. Using hierarchical neural networks for diagnosing and predicting the condition of a nuclear reactor. In Proceedings of ICANN’95. Paris, 1995.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • P. Weller
    • 1
    • 2
  • A. Thompson
    • 2
  • R. Summers
    • 1
  1. 1.Department of System ScienceCity UniversityLondonUK
  2. 2.Department of Nuclear Science & TechnologyRoyal Naval CollegeGreenwich, LondonUK

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