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Class of Hamiltonian neural networks

De Wilde, Philippe (1993) Class of Hamiltonian neural networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 47 (2). pp. 1392-1396. ISSN 1063-651X. (doi:10.1103/PhysRevE.47.1392) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:93397)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1103/PhysRevE.47.1392

Abstract

We investigate analog neural networks. They have continuous state variables that depend continuously on time. Although they all have an energy function, not all can have their dynamics derived from a Hamiltonian. Some necessary conditions are given for the network to have Hamiltonian dynamics. We give an example and, using symplectic transformations, describe a whole class of neural networks with Hamiltonian dynamics.

Item Type: Article
DOI/Identification number: 10.1103/PhysRevE.47.1392
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Philippe De Wilde
Date Deposited: 20 Dec 2022 09:37 UTC
Last Modified: 09 Jan 2023 16:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93397 (The current URI for this page, for reference purposes)

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