Relative order defines a topology for recurrent networks

Swanston, D.J., Kambhampati, C., Manchanda, S., Tham, M. and Warwick, K. (1995) Relative order defines a topology for recurrent networks. In: Fourth International Conference on Artificial Neural Networks, 26-28 June 1995, Cambridge, UK, pp. 256-261. doi: 10.1049/cp:19950564

Abstract/Summary

This paper uses techniques from control theory in the analysis of trained recurrent neural networks. Differential geometry is used as a framework, which allows the concept of relative order to be applied to neural networks. Any system possessing finite relative order has a left-inverse. Any recurrent network with finite relative order also has an inverse, which is shown to be a recurrent network.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/21670
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Science
Uncontrolled Keywords	Hopfield network, control theory, differential geometry, finite relative order, left inverse, neural network architecture, neural network training, neurocontrol, recurrent network topology, recurrent neural networks, relative order
Download/View statistics	View download statistics for this item