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Relative order defines a topology for recurrent networks

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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, https://doi.org/10.1049/cp:19950564.

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To link to this item 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)
Refereed:Yes
Divisions:Science
ID Code:21670
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
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