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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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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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creator	Lipo Wang Wen Liu Haixiang Shi Zurada, J.M.
description	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
doi_str_mv	10.1109/TCSII.2007.892399
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source	IEEE Electronic Library (IEL) Journals
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
title	Cellular Neural Networks With Transient Chaos
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