Shunting Inhibitory Cellular Neural Networks with Compartmental Unpredictable Coefficients and Inputs

Shunting inhibitory cellular neural networks with compartmental periodic unpredictable coefficients and inputs is the focus of this research. A new algorithm suggested, to enlarge set known functions by applying diagonalization in arguments several variables. Sufficient conditions for existence uniqueness exponentially stable Poisson outputs are obtained. To attain theoretical results, included intervals method contraction mapping principle used. Appropriate examples numerical simulations that support results provided. It shown how dynamics network depend on a characteristic, degree periodicity.