Research Papers

Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons

[+] Author and Article Information
Elham Javidmanesh

Department of Applied Mathematics,
Ferdowsi University of Mashhad,
Mashhad 9177948974, Iran
e-mails: javidmanesh@ferdowsi.um.ac.ir; e_javidmanesh@yahoo.com

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 6, 2015; final manuscript received February 11, 2017; published online June 5, 2017. Assoc. Editor: Azim Eskandarian.

J. Dyn. Sys., Meas., Control 139(8), 081018 (Jun 05, 2017) (5 pages) Paper No: DS-15-1489; doi: 10.1115/1.4036229 History: Received October 06, 2015; Revised February 11, 2017

In this paper, delayed bidirectional associative memory (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.

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Grahic Jump Location
Fig. 1

A family of periodic solutions bifurcate from the origin and Hopf bifurcation occurs




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