Stability analysis for delayed Cohen-Grossberg Clifford-valued neutral-type neural networks

The aim of this study is to explore the global stability of Cohen-Grossberg Clifford-valued neutral-type neural network models with time delays. In order to achieve the aim of this paper, and to solve the non-commutativity problem caused by Clifford numbers multiplication, the original Clifford-valued system is first decomposed into 2(m) n-dimensional real-valued systems. Some sufficient criteria for the global stability of the addressed network models are established by constructing an appropriate Lyapunov functional. The established stability conditions have not been affected by the neutral delay and time delay values. The proposed method and results of this paper are new. The feasibility of the stability criteria obtained are verified using two numerical examples.

Automated summary

This paper aims to explore the global stability of Cohen-Grossberg Clifford-valued neutral-type neural network models with time delays. By decomposing the Clifford-valued system into real-valued systems and constructing an appropriate Lyapunov functional, some sufficient criteria for the global stability of the network models have been established, unaffected by neutral delay and time delay values.