0
research-article

Global stability and bifurcation in delayed BAM neural networks with an arbitrary number of neurons

[+] Author and Article Information
Elham Javidmanesh

Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
javidmanesh@ferdowsi.um.ac.ir

1Corresponding author.

ASME doi:10.1115/1.4036229 History: Received October 06, 2015; Revised February 11, 2017

Abstract

In this paper, delayed BAM neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.

Copyright (c) 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In