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Inverse model control using recurrent networks

Author

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  • Kambhampati, C.
  • Craddock, R.J.
  • Tham, M.
  • Warwick, K.

Abstract

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Suggested Citation

  • Kambhampati, C. & Craddock, R.J. & Tham, M. & Warwick, K., 2000. "Inverse model control using recurrent networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 181-199.
    • Handle: RePEc:eee:matcom:v:51:y:2000:i:3:p:181-199
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    Cited by:

    1. Dimopoulos, K.P. & Kambhampati, C. & Craddock, R., 2000. "Efficient recurrent neural network training incorporating a priori knowledge," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 52(2), pages 137-162.

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