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Inverse model control using recurrent networks

Kambhampati, C., Craddock, R. J., Tham, M. and Warwick, K. (2000) Inverse model control using recurrent networks. Mathematics and Computers in Simulation, 51 (3-4). pp. 181-199. ISSN 0378-4754

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To link to this article DOI: 10.1016/S0378-4754(99)00116-0

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:17798
Uncontrolled Keywords:Relative order, left-inverses, neural networks, inverse model control
Publisher:Elsevier

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