@inproceedings{2862853eb4a24dd3b07bd867cbd944f1,
title = "Maximal domains of attraction in a Hopfield neural network with learning",
abstract = "In this paper we describe an approach to maximizing the domains of attraction for equilibria in a Hopfield neural network with learning. The concept of learning in a Hopfield net is introduced and a method is given to construct a hetero-associative memory using a Hopfield net that `learns' the correct weights required to store arbitrarily specified input/output pairs. By proper choice of the feedback gains in the weight update equation it is possible to maximize the domain of attraction for the stored equilibrium points, resulting in a robust associative memory.",
author = "Moore, {Kevin L.} and Naidu, {D. Subbaram}",
year = "1993",
doi = "10.23919/acc.1993.4793428",
language = "English (US)",
isbn = "0780308611",
series = "American Control Conference",
publisher = "Publ by IEEE",
pages = "2894--2896",
booktitle = "American Control Conference",
note = "Proceedings of the 1993 American Control Conference ; Conference date: 02-06-1993 Through 04-06-1993",
}