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A practical approach for training dynamic recurrent neural networks: use of Apriori information

Craddock, 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.

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To link to this article DOI: 10.1049/cp:19980249

Abstract/Summary

Presents 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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:21625
Uncontrolled Keywords:a priori information, chemotaxis, dynamic recurrent neural networks, state space system, training algorithm

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