CHAOS AND TWO-TORI IN A NEW FAMILY OF 4-CNNS

2007 ◽  
Vol 17 (03) ◽  
pp. 953-963 ◽  
Author(s):  
XIAO-SONG YANG ◽  
YAN HUANG
Keyword(s):  
Neural Networks ◽  
Limit Cycle ◽  
Limit Cycles ◽  
Cellular Neural Networks ◽  
Lyapunov Spectrum ◽  
Regular Array ◽  
Poincaré Maps ◽  
One Dimensional ◽  
Poincare Maps ◽  
New Family

In this paper we demonstrate chaos, two-tori and limit cycles in a new family of Cellular Neural Networks which is a one-dimensional regular array of four cells. The Lyapunov spectrum is calculated in a range of parameters, the bifurcation plots are presented as well. Furthermore, we confirm the nature of limit cycle, chaos and two-tori by studying Poincaré maps.

HYPERCHAOS IN A NEW FAMILY OF SIMPLE CNNS

2006 ◽  
Vol 16 (11) ◽  
pp. 3341-3348
Author(s):  
YAN HUANG ◽  
XIAO-SONG YANG
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
Lyapunov Spectrum ◽  
Phase Portraits ◽  
Regular Array ◽  
One Dimensional ◽  
New Family

In this paper we demonstrate hyperchaotic dynamics in a new family of simple Cellular Neural Networks (CNNs) which is a one-dimensional regular array of four cells. The Lyapunov spectrum is calculated in a range of parameters, the bifurcation plots and several important phase portraits are presented as well.

HORSESHOE CHAOS IN CELLULAR NEURAL NETWORKS

2006 ◽  
Vol 16 (01) ◽  
pp. 157-161 ◽  
Author(s):  
XIAO-SONG YANG ◽  
QINGDU LI
Keyword(s):  
Neural Networks ◽  
Cross Section ◽  
Cellular Neural Networks ◽  
Computer Assisted ◽  
Poincaré Maps ◽  
Poincare Maps ◽  
Computer Assisted Proof ◽  
Low Dimensional

In this paper, we demonstrate chaos in low dimensional cellular neural networks for some weight matrices. To verify chaoticity of the dynamics in these cellular neural networks, we consider a cross-section properly chosen for the attractors obtained and study the dynamics of the corresponding Poincaré maps, and rigorously verify the existence of horseshoe in the manner of computer-assisted proof arguments.

Poincaré Maps of “ <”-Shape Planar Piecewise Linear Dynamical Systems with a Saddle

2019 ◽  
Vol 29 (12) ◽  
pp. 1950165
Author(s):  
Qianqian Zhao ◽  
Jiang Yu
Keyword(s):  
Dynamical Systems ◽  
Lower Bound ◽  
Normal Form ◽  
Limit Cycle ◽  
Limit Cycles ◽  
Inflection Point ◽  
Piecewise Linear ◽  
Poincaré Maps ◽  
Linear Dynamical Systems ◽  
Poincare Maps

It is important in the study of limit cycles to investigate the properties of Poincaré maps of discontinuous dynamical systems. In this paper, we focus on a class of planar piecewise linear dynamical systems with “[Formula: see text]”-shape regions and prove that the Poincaré map of a subsystem with a saddle has at most one inflection point which can be reached. Furthermore, we show that one class of such systems with a saddle-center has at least three limit cycles; a class of such systems with saddle and center in the normal form has at most one limit cycle which can be reached; and a class of such systems with saddle and center at the origin has at most three limit cycles with a lower bound of two. We try to reveal the reasons for the increase of the number of limit cycles when the discontinuity happens to a system.

COMPUTER ASSISTED VERIFICATION OF CHAOS IN THREE-NEURON CELLULAR NEURAL NETWORKS

2007 ◽  
Vol 17 (12) ◽  
pp. 4381-4386 ◽  
Author(s):  
QUAN YUAN ◽  
XIAO-SONG YANG
Keyword(s):  
Neural Networks ◽  
Topological Entropy ◽  
Chaotic Behavior ◽  
Cellular Neural Networks ◽  
Computer Assisted ◽  
Topological Horseshoe ◽  
Poincaré Maps ◽  
New Class ◽  
Numerical Studies ◽  
Connection Matrices

In this paper, we study a new class of simple three-neuron chaotic cellular neural networks with very simple connection matrices. To study the chaotic behavior in these cellular neural networks demonstrated in numerical studies, we resort to Poincaré section and Poincaré map technique and present a rigorous verification of the existence of horseshoe chaos using topological horseshoe theory and the estimate of topological entropy in derived Poincaré maps.

EXISTENCE ANALYSIS OF MOSAIC SOLUTIONS FOR ONE-DIMENSIONAL CELLULAR NEURAL NETWORKS

2006 ◽  
Vol 16 (12) ◽  
pp. 3669-3677 ◽  
Author(s):  
YUN-QUAN KE ◽  
FENG-YAN ZHOU
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Cellular Neural Networks ◽  
One Dimensional

In this letter, the mosaic solutions of one-dimensional Cellular Neural Networks system (CNNs) are investigated. Three types of parameters, the synaptic weights, the input terms and the threshold are properly chosen in terms of Chua's driving-point plot. Moreover, we give sufficient conditions for the existence of the mosaic solutions.

SPATIAL DISORDER OF CELLULAR NEURAL NETWORKS — WITH BIASED TERM

2002 ◽  
Vol 12 (03) ◽  
pp. 525-534 ◽  
Author(s):  
JUNG-CHAO BAN ◽  
SONG-SUN LIN ◽  
CHENG-HSIUNG HSU
Keyword(s):  
Neural Networks ◽  
Stationary Solutions ◽  
Cellular Neural Networks ◽  
One Dimensional ◽  
Spatial Entropy ◽  
Stable Stationary Solutions

This study describes the spatial disorder of one-dimensional Cellular Neural Networks (CNN) with a biased term by applying the iteration map method. Under certain parameters, the map is one-dimensional and the spatial entropy of stable stationary solutions can be obtained explicitly as a staircase function.

Limit cycle analysis of the verge and foliot clock escapement using impulsive differential equations and Poincaré maps

2003 ◽  
Vol 76 (17) ◽  
pp. 1685-1698 ◽  
Author(s):  
Alexander V. Roup ◽  
Dennis S. Bernstein ◽  
Sergey G. Nersesov ◽  
Wassim M. Haddad ◽  
VijaySekhar Chellaboina
Keyword(s):  
Differential Equations ◽  
Limit Cycle ◽  
Impulsive Differential Equations ◽  
Poincaré Maps ◽  
Poincare Maps

PIECEWISE TWO-DIMENSIONAL MAPS AND APPLICATIONS TO CELLULAR NEURAL NETWORKS

2004 ◽  
Vol 14 (07) ◽  
pp. 2223-2228 ◽  
Author(s):  
HSIN-MEI CHANG ◽  
JONG JUANG
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
Two Dimensional ◽  
Smale Horseshoe ◽  
Linear Map ◽  
One Dimensional ◽  
Spatial Chaos

Of concern is a two-dimensional map T of the form T(x,y)=(y,F(y)-bx). Here F is a three-piece linear map. In this paper, we first prove a theorem which states that a semiconjugate condition for T implies the existence of Smale horseshoe. Second, the theorem is applied to show the spatial chaos of one-dimensional Cellular Neural Networks. We improve a result of Hsu [2000].

SPATIAL DISORDER OF CNN — WITH ASYMMETRIC OUTPUT FUNCTION

2001 ◽  
Vol 11 (08) ◽  
pp. 2085-2095 ◽  
Author(s):  
JUNG-CHAO BAN ◽  
KAI-PING CHIEN ◽  
SONG-SUN LIN ◽  
CHENG-HSIUNG HSU
Keyword(s):  
Neural Networks ◽  
Steady State ◽  
Three Dimensional ◽  
Cellular Neural Networks ◽  
Output Function ◽  
One Dimensional ◽  
Spatial Entropy ◽  
The One ◽  
Steady State Solutions

This investigation will describe the spatial disorder of one-dimensional Cellular Neural Networks (CNN). The steady state solutions of the one-dimensional CNN can be replaced as an iteration map which is one dimensional under certain parameters. Then, the maps are chaotic and the spatial entropy of the steady state solutions is a three-dimensional devil-staircase like function.

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