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General transient length upper bound for recurrent neural networks

Ho, A.M.C.L., De Wilde, Philippe (1995) General transient length upper bound for recurrent neural networks. In: Lecture Notes in Computer Science. From Natural to Artificial Neural Computation. IWANN 1995. 930. pp. 202-208. Springer ISBN 978-3-540-59497-0. (doi:10.1007/3-540-59497-3_176) (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:93396)

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/10.1111/10.1007/3-540-59497-3_176

Abstract

We show how to construct a Lyapunov function for a discrete recurrent neural network using the variable-gradient method, This method can also be used to obtain the Hopfield energy function. Using our Lyapunov function, we compute an upper bound for the transient length for our neural network dynamics. We also show how our Lyapunov function can provide insights into the effect that the introduction of self-feedback weights to our neural network has on the sizes of the basins of attraction of the equilibrium points of the neural network state space.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/3-540-59497-3_176
Uncontrolled keywords: neural networks
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Philippe De Wilde
Date Deposited: 03 Jan 2023 16:36 UTC
Last Modified: 04 Jan 2023 13:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93396 (The current URI for this page, for reference purposes)

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