Study on Discreted Fifth-Order CNN Hyper-Chaos Synchronization Reduced Order Observer

2011 ◽  
Vol 135-136 ◽  
pp. 960-963
Author(s):  
Jie Xu ◽  
Juan Chang ◽  
Lian Zhang ◽  
Yong Sun
Keyword(s):  
Neural Networks ◽  
Continuous Time ◽  
Secure Communication ◽  
Chaos Synchronization ◽  
State Observer ◽  
Cellular Neural Networks ◽  
Reduced Order ◽  
Reduced Order Observer ◽  
Fifth Order ◽  
Order State

Secure communication with the hyper-chaos synchronization approach is the popular investigation. In this paper the fifth-order Cellular Neural Networks(CNN) hyper-chaos equation of continuous time was discreted. The innovation was that the synchronization of the hyper-chaos system was realized with the discreted reduced order state observer. Finally, the system stabilizing to zero after fugacious transition and achieving synchronization were validated by simulations, which showed the algorithm’s availability.

DYNAMICS FOR DISCRETE-TIME CELLULAR NEURAL NETWORKS

2004 ◽  
Vol 14 (08) ◽  
pp. 2667-2687 ◽  
Author(s):  
SHYAN-SHIOU CHEN ◽  
CHIH-WEN SHIH
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Energy Function ◽  
Continuous Time ◽  
Chaotic Dynamics ◽  
Homoclinic Orbits ◽  
Cellular Neural Networks ◽  
Attracting Set ◽  
Transversal Homoclinic Orbits

This presentation investigates the dynamics of discrete-time cellular neural networks (DT-CNN). In contrast to classical neural networks that are mostly gradient-like systems, DT-CNN possesses both complete stability and chaotic behaviors as different parameters are considered. An energy-like function which decreases along orbits of DT-CNN as well as the existence of a globally attracting set are derived. Complete stability can then be concluded, with further analysis on the sets on which the energy function is constant. The formations of saturated stationary patterns for DT-CNN are shown to be analogous to the ones in continuous-time CNN. Thus, DT-CNN shares similar properties with continuous-time CNN. By confirming the existence of snap-back repellers, hence transversal homoclinic orbits, we also conclude that DT-CNN with certain parameters exhibits chaotic dynamics, according to the theorem by Marotto.

CHARACTERISTIC OF MUTUALLY COUPLED TWO-LAYER CNN AND ITS STABILITY

2003 ◽  
Vol 12 (04) ◽  
pp. 473-490 ◽  
Author(s):  
ZONGHUANG YANG ◽  
YOSHIFUMI NISHIO ◽  
AKIO USHIDA
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Single Layer ◽  
Cellular Neural Networks ◽  
Center Point ◽  
The Stability ◽  
Point Detection

This paper presents some interesting image processing applications with the mutually coupled two-layer Cellular Neural Networks (CNNs). We found that the two-layer CNNs are very useful compared to single layer CNNs in some applications such as center point detection, skeletonization, and so on. We also focus our discussions on both their transients and operations. In addition, the stability of the two-layer CNNs with mutually coupled symmetric templates is also discussed based on those of decoupling CNN technique.

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