title: Reconstruction of noisy patterns by bistable gradient neural-like network.
abstract: An attractor bistable gradient neural-like network (BGN) described in Chinarov and Menzinger (BioSystems 55 (2000) 137) is applied to restoration of unknown patterns, which have been highly corrupted by multiplicative and additive Gaussian white noise. This becomes possible due to competitive advantage of BGN that derives from it: nice generalization capabilities, existence of the unique attractor with the lowest energy that is worked out when several patterns are stored by the network, and fast guaranteed convergence to this attractor.
authors.affiliation: Department of Chemistry, University of Toronto, 80 St George Street, Toronro, Ont, Canada M5S 3H6. vchinaro@chem.utoronto.ca
authors.forename: Vladimir
authors.initials: V
authors.lastname: Chinarov
authors.forename: Michael
authors.initials: M
authors.lastname: Menzinger
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer