Identification of nonlinear systems with a dynamic recurrent neural networkDelgado, 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, https://doi.org/10.1049/cp:19950575. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1049/cp:19950575 Abstract/SummaryTwo 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.
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