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Identification of nonlinear systems with a dynamic recurrent neural network

Delgado, A., Kambhampati, C. and Warwick, K. (1995) Identification of nonlinear systems with a dynamic recurrent neural network. In: Fourth International Conference on Artificial Neural Networks, 26-28 June 1995, Cambridge, UK, pp. 318-322.

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To link to this article DOI: 10.1049/cp:19950575

Abstract/Summary

Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input-output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:21667
Uncontrolled Keywords:dynamic recurrent neural network, identification, identified network, nonlinear systems, states

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