Robustness Analysis of Fuzzy Cellular Neural Network With Deviating Argument and Stochastic Disturbances

Robustness analysis of fuzzy cellular neural networks with deviating arguments and stochastic disturbances is the main topic of discussion in this paper. The issue at hand is what the upper bounds of the disturbances and deviating intervals for the fuzzy cellular neural network can withstand before losing its stability. We solve these problems by using Gronwall-Bellman lemma and some inequality techniques. The theoretical results point that for an exponentially stable fuzzy cellular neural network, the perturbed fuzzy cellular neural network still keep its globally exponential stability if the upper bound of the length of deviating intervals or the intensity of stochastic disturbances is less than the upper bound derived in this paper. A number of numerical cases are offered to support the availability of conjectural values.

Automated summary

This paper discusses the robustness analysis of fuzzy cellular neural networks with deviating arguments and stochastic disturbances. The main focus is on determining the upper bounds of disturbances and deviating intervals that the network can withstand without losing stability. By using the Gronwall-Bellman lemma and inequality techniques, these problems are solved. The theoretical results indicate that for an exponentially stable fuzzy cellular neural network, the perturbed network can maintain its globally exponential stability if the upper bound of deviating intervals or the intensity of stochastic disturbances is less than the bound derived in this paper. Several numerical cases are provided to support the conjectural values.