Bifurcation and Chaos in Delayed Cellular Neural Network Model


This paper deals with control of chaotic behavior of a delayed Cellular Neural Network (DCNN) model which is a one-dimensional regular array of four cells with continuous activation function. We investigate different dynamical behaviors including limit cycle, torus, and chaos for different range of weight parameters of the system. Regarding synaptic weight as parameter, Hopf bifurcations are obtained in the system without delay. In the delayed model condition for the Global asymptotic stability of the equilibrium point is presented. Numerical simulation and results are given to show the role of delay in chaos control of the CNNs.

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Das, P. and Kundu, A. (2014) Bifurcation and Chaos in Delayed Cellular Neural Network Model. Journal of Applied Mathematics and Physics, 2, 219-224. doi: 10.4236/jamp.2014.25027.

Conflicts of Interest

The authors declare no conflicts of interest.


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