Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing

2013 ◽  
Vol 25 (2) ◽  
pp. 291-296 ◽  
Author(s):  
Shukai Duan ◽  
Xiaofang Hu ◽  
Lidan Wang ◽  
Shiyong Gao ◽  
Chuandong Li
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Networks

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.

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.

ADVANCED IMAGE PROCESSING CELLULAR NEURAL NETWORKS

2007 ◽  
Vol 17 (04) ◽  
pp. 1109-1150 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Networks ◽  
Visual Illusions ◽  
Cognitive Engineering ◽  
Neural Basis ◽  
Brain Functions ◽  
Texture Generation ◽  
High Level ◽  
Neural Field Models

Many useful and well-known image processing templates for cellular neural networks (CNN's) can be derived from neural field models, thereby providing a neural basis for the CNN paradigm. The potential ability of multitasking image processing is investigated by using these templates. Many visual illusions are simulated via CNN image processing. The ability of the CNN to mimic such high-level brain functions suggests possible applications of the CNN in cognitive engineering. Furthermore, two kinds of painting-like image processings, namely, texture generation and illustration style transformation are investigated.

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