A practical approach for training dynamic recurrent neural networks: use of Apriori informationCraddock, R. J., Kambhampati, C., Tham, M. and Warwick, K. (1998) A practical approach for training dynamic recurrent neural networks: use of Apriori information. In: The UKACC '98 Conference on Control, 1-4 September 1998, Swansea, UK, pp. 324-329, https://doi.org/10.1049/cp:19980249. 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:19980249 Abstract/SummaryPresents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.
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