Stabilize Unstable Equilibrium Points for A New Class of Simple 3-Neuron Chaotic Cellular Neural Network

2006 ◽  
Author(s):  
Ping Zhou ◽  
Qiong Huang ◽  
Jiang-tao Li
Keyword(s):  
Neural Network ◽  
Equilibrium Points ◽  
Cellular Neural Network ◽  
Unstable Equilibrium ◽  
New Class

scholarly journals Hidden Multistability in a Memristor-Based Cellular Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Birong Xu ◽  
Hairong Lin ◽  
Guangyi Wang
Keyword(s):  
Neural Network ◽  
Simulation Analysis ◽  
Equilibrium Points ◽  
Cellular Neural Network ◽  
Nonlinear Phenomena ◽  
Hidden Attractors ◽  
Coexisting Attractors ◽  
Dynamical Behaviors ◽  
Circuit Simulations

In this paper, we report a novel memristor-based cellular neural network (CNN) without equilibrium points. Dynamical behaviors of the memristor-based CNN are investigated by simulation analysis. The results indicate that the system owns complicated nonlinear phenomena, such as hidden attractors, coexisting attractors, and initial boosting behaviors of position and amplitude. Furthermore, both heterogeneous multistability and homogenous multistability are found in the CNN. Finally, Multisim circuit simulations are performed to prove the chaotic characteristics and multistability of the system.

scholarly journals Dynamic properties of cellular neural networks

1993 ◽  
Vol 6 (2) ◽  
pp. 107-116 ◽  
Author(s):  
Angela Slavova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dynamic Properties ◽  
Cellular Neural Network ◽  
State Equation ◽  
Cellular Neural Networks ◽  
New Approach ◽  
New Class ◽  
Continuous State Space ◽  
Continuous State

Dynamic behavior of a new class of information-processing systems called Cellular Neural Networks is investigated. In this paper we introduce a small parameter in the state equation of a cellular neural network and we seek for periodic phenomena. New approach is used for proving stability of a cellular neural network by constructing Lyapunov's majorizing equations. This algorithm is helpful for finding a map from initial continuous state space of a cellular neural network into discrete output. A comparison between cellular neural networks and cellular automata is made.

scholarly journals Analyzing stability of equilibrium points in impulsive neural network models involving generalized piecewise alternately advanced and retarded argument

2021 ◽  
Author(s):  
Kuo-Shou Chiu
Keyword(s):  
Neural Network ◽  
Global Exponential Stability ◽  
Equilibrium Points ◽  
Sufficient Conditions ◽  
Network Models ◽  
Cellular Neural Network ◽  
Retarded Argument ◽  
Impulsive Systems ◽  
Neural Network Models ◽  
Banach’S Fixed Point Theorem

In this paper, we investigate the models of the impulsive cellular neural network with piecewise alternately advanced and retarded argument of generalized argument (in short IDEPCAG). To ensure the existence, uniqueness and global exponential stability of the equilibrium state, several new sufficient conditions are obtained, which extend the results of the previous literature. The method is based on utilizing Banach’s fixed point theorem and a new IDEPCAG’s Gronwall inequality. The criteria given are easy to check and when the impulsive effects do not affect, the results can be extracted from those of the non-impulsive systems. Typical numerical simulation examples are used to show the validity and effectiveness of proposed results. We end the article with a brief conclusion.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

Sigma–delta cellular neural network for 2D modulation

2008 ◽  
Vol 21 (2-3) ◽  
pp. 349-357 ◽  
Author(s):  
Hisashi Aomori ◽  
Tsuyoshi Otake ◽  
Nobuaki Takahashi ◽  
Mamoru Tanaka
Keyword(s):  
Neural Network ◽  
Cellular Neural Network ◽  
Sigma Delta
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