Paper Info
Title
Multistability of delayed neural networks with hard-limiter saturation nonlinearities.
Abstract
The paper considers a class of nonsmooth neural networks where hard-limiter saturation nonlinearities are used to constrain solutions of a linear system with concentrated and distributed delays to evolve within a closed hypercube of Rn. Such networks are termed delayed linear systems in saturated mode (D-LSSMs) and they are a generalization to the delayed case of a relevant class of neural networks previously introduced in the literature. The paper gives a rigorous foundation to the D-LSSM model and then it provides a fundamental result on convergence of solutions toward equilibrium points in the case where there are nonsymmetric cooperative (nonnegative) interconnections between neurons. The result ensures convergence for any finite value of the maximum delay and is physically robust with respect to perturbations of the interconnections. More importantly, it encompasses situations where there exist multiple stable equilibria, thus guaranteeing multistability of cooperative D-LSSMs. From an application viewpoint the delays in combination with the property of multistability make D-LSSMs potentially useful in the fields of associative memories, motion detection and processing of temporal patterns.
Year
DOI
Venue
2018
10.1016/j.neucom.2018.03.006
Neurocomputing
Keywords
Field
DocType
Delayed neural networks,Nonsmooth dynamical systems,Cooperative systems,Multistability,Hard-limiter saturation nonlinearities
Convergence (routing),Topology,Associative property,Linear system,Motion detection,Pattern recognition,Equilibrium point,Artificial intelligence,Multistability,Artificial neural network,Mathematics,Hypercube
Journal
Volume
ISSN
Citations 
293
0925-2312
2
PageRank 
References 
Authors
0.36
31
4
Name
Order
Citations
PageRank
Mauro Di Marco120518.38
Mauro Forti239836.80
Massimo Grazzini313111.01
Luca Pancioni420717.58