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. 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)
URI	https://reading-clone.eprints-hosting.org/id/eprint/21625
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Science
Uncontrolled Keywords	a priori information, chemotaxis, dynamic recurrent neural networks, state space system, training algorithm
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