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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. 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.
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| Item Type | Conference or Workshop Item (Paper) |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/21670 |
| Identification Number/DOI | 10.1049/cp:19950564 |
| 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 |
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