Cellular Neural Networks With Transient Chaos

A new model of cellular neural networks (CNNs) with transient chaos is proposed by adding negative self-feedbacks into CNNs after transforming the dynamic equation to discrete time via Euler's method. The simulation on the single neuron model shows stable fix points, bifurcation and chaos. Henc...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. 2, Analog and digital signal processing Analog and digital signal processing, 2007-05, Vol.54 (5), p.440-444
Main Authors: Lipo Wang, Wen Liu, Haixiang Shi, Zurada, J.M.
Format: Article
Language:English
Subjects:
Bifurcation
Cellular
Cellular neural networks
cellular neural networks (CNNs)
Chaos
Chaos theory
Computational modeling
Computer simulation
Dynamical systems
Dynamics
Hopfield neural networks
Mathematical analysis
Neural networks
Neurons
Power engineering and energy
Simulated annealing
Stochastic processes
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Summary:A new model of cellular neural networks (CNNs) with transient chaos is proposed by adding negative self-feedbacks into CNNs after transforming the dynamic equation to discrete time via Euler's method. The simulation on the single neuron model shows stable fix points, bifurcation and chaos. Hence, this new CNN model has richer and more flexible dynamics, and therefore may possess better capabilities of solving various problems, compared to the conventional CNN with only stable dynamics
ISSN:1549-7747
1057-7130
1558-3791
DOI:10.1109/TCSII.2007.892399