Existence of Anti-Periodic Solution for Delayed Cellular Neural Networks with Impulsive Effects

2013 ◽  
Vol 477-478 ◽  
pp. 1499-1503
Author(s):  
Lin Li Zhang ◽  
Rui Li Fan ◽  
An Ping Liu ◽  
Wei Fang Yang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Periodic Solution ◽  
Existence And Uniqueness ◽  
Cellular Neural Networks ◽  
Existence Of Solution ◽  
Impulsive Effects ◽  
Iterative Analysis

As an important tool to study practical problems of biology, engineering and image processing, the cellular neural networks (CNNs) has caused more and more attention. Some interesting results about the existence of solution for cellular neural networks have been obtained. In this paper, by means of iterative analysis, the existence and uniqueness of anti-periodic solution of delayed cellular neural networks with impulsive effects are considered. Some new results are obtained.

Existence and Stability of Periodic Solution for Impulsive Hopfield Cellular Neural Networks with Distributed Delays

2013 ◽  
Vol 275-277 ◽  
pp. 2601-2605
Author(s):  
Lin Li Zhang ◽  
Rui Li Fan ◽  
An Ping Liu ◽  
Li Xiao
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Periodic Solution ◽  
Equilibrium Point ◽  
Cellular Neural Networks ◽  
Existence Of Solution ◽  
Distributed Delays ◽  
Uniform Stability ◽  
Iterative Analysis ◽  
Existence And Stability

As an important tool to study practical problems of biology, engineering and image processing, the cellular neural networks (CNNs) has caused more and more attention. Some interesting results about the existence of solution for cellular neural networks have been obtained. In this paper, by means of iterative analysis, the existence of periodic solution and the uniform stability of the equilibrium point of impulsive Hopfield cellular neural networks with distributed delays are considered. Some new results are obtained.

scholarly journals Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays

2011 ◽  
Vol 21 (4) ◽  
pp. 649-658 ◽  
Author(s):  
Qianhong Zhang ◽  
Lihui Yang ◽  
Daixi Liao
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Exponential Stability ◽  
Differential Inequality ◽  
Sufficient Conditions ◽  
Coincidence Degree ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
Inequality Technique

Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays Fuzzy cellular neural networks with time-varying delays are considered. Some sufficient conditions for the existence and exponential stability of periodic solutions are obtained by using the continuation theorem based on the coincidence degree and the differential inequality technique. The sufficient conditions are easy to use in pattern recognition and automatic control. Finally, an example is given to show the feasibility and effectiveness of our methods.

Switched-Current Cellular Neural Networks for Image Processing

2011 ◽  
pp. 459-486
Author(s):  
Angel Rodriguez-Vazquez ◽  
Servando Espejo ◽  
Jose Huertas ◽  
Rafael Domuiguez-Castro
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Networks ◽  
Switched Current

scholarly journals Zagreb Connection Numbers for Cellular Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Jia-Bao Liu ◽  
Zahid Raza ◽  
Muhammad Javaid
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Mathematical Models ◽  
Structural Properties ◽  
Cellular Neural Networks ◽  
Molecular Networks ◽  
Graph Invariants ◽  
Connection Number ◽  
Sensory Motor ◽  
3D Surfaces

Neural networks in which communication works only among the neighboring units are called cellular neural networks (CNNs). These are used in analyzing 3D surfaces, image processing, modeling biological vision, and reducing nonvisual problems of geometric maps and sensory-motor organs. Topological indices (TIs) are mathematical models of the (molecular) networks or structures which are presented in the form of numerical values, constitutional formulas, or numerical functions. These models predict the various chemical or structural properties of the under-study networks. We now consider analogous graph invariants, based on the second connection number of vertices, called Zagreb connection indices. The main objective of this paper is to compute these connection indices for the cellular neural networks (CNNs). In order to find their efficiency, a comparison among the obtained indices of CNN is also performed in the form of numerical tables and 3D plots.

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