TY - JOUR
T1 - A study of cellular neural networks with vertex-edge topological descriptors
AU - Husain, Sadia
AU - Imran, Muhammad
AU - Ahmad, Ali
AU - Ahmad, Yasir
AU - Elahi, Kashif
N1 - Funding Information:
Funding Statement: This research is supported by the University program of Advanced Research (UPAR) and UAEU-AUA grants of United Arab Emirates University (UAEU) via Grant No. G00003271 and Grant No. G00003461.
Publisher Copyright:
© 2022 Tech Science Press. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The Cellular Neural Network (CNN) has various parallel processing applications, image processing, non-linear processing, geometric maps, high-speed computations. It is an analog paradigm, consists of an array of cells that are interconnected locally. Cells can be arranged in different configurations. Each cell has an input, a state, and an output. The cellular neural network allows cells to communicate with the neighbor cells only. It can be represented graphically; cells will represent by vertices and their interconnections will represent by edges. In chemical graph theory, topological descriptors are used to study graph structure and their biological activities. It is a single value that characterizes the whole graph. In this article, the vertex-edge topological descriptors have been calculated for cellular neural network. Results can be used for cellular neural network of any size. This will enhance the applications of cellular neural network in image processing, solving partial differential equations, analyzing 3D surfaces, sensory-motor organs, and modeling biological vision.
AB - The Cellular Neural Network (CNN) has various parallel processing applications, image processing, non-linear processing, geometric maps, high-speed computations. It is an analog paradigm, consists of an array of cells that are interconnected locally. Cells can be arranged in different configurations. Each cell has an input, a state, and an output. The cellular neural network allows cells to communicate with the neighbor cells only. It can be represented graphically; cells will represent by vertices and their interconnections will represent by edges. In chemical graph theory, topological descriptors are used to study graph structure and their biological activities. It is a single value that characterizes the whole graph. In this article, the vertex-edge topological descriptors have been calculated for cellular neural network. Results can be used for cellular neural network of any size. This will enhance the applications of cellular neural network in image processing, solving partial differential equations, analyzing 3D surfaces, sensory-motor organs, and modeling biological vision.
KW - Cellular neural networks
KW - Degree
KW - Topological indices
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U2 - 10.32604/cmc.2022.020384
DO - 10.32604/cmc.2022.020384
M3 - Article
AN - SCOPUS:85116059499
SN - 1546-2218
VL - 70
SP - 3433
EP - 3447
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 2
ER -