Mathematical formulation and irregularity topological indices of cellular neural network system

The Cellular Neural Network (CNN) execute high-speed calculations, geometric maps, non-linear processing, image processing, and other parallel processing tasks. It is an analogue paradigm made up of a collection of cells that are locally coupled to one another. Different arrangements of cells are possible. An input, a state, and an output are present in each cell. Only neighboring cells can communicate with one another in a cellular neural network. It can be seen graphically, with the interconnections between cells represented by edges and the cells themselves by vertices. Uniquely suited for high-speed parallel signal processing are cellular neural networks. The continuous-time aspect of cellular neural networks enables real-time signal processing, while the local interconnection feature makes them particularly suited for VLSI implementation. Cellular neural networks combine the best features of both worlds. Topological descriptors are employed in chemical graph theory to investigate graph structure and biological activity. The entire graph can be described by a single value. The irregular topological indices for CNN have been computed in this paper. The obtained results are useful to CNN networks of any size. The applications of CNN in image processing, partial differential equations, 3D surface analysis, sensory-motor organ modelling, and biological vision modelling are all improved by these studies.