Mean-square stability of stochastic Clifford-valued Cohen-Grossberg neural networks with variable coefficients and time-varying delays

In this paper, the mean-square stability of the stochastic Clifford-valued Cohen-Grossberg neural networks (SCVCGNNs) with variable coefficients and multiple time-varying delays is considered. Rather than disassembling the SCVCGNNs into real-valued or complex-valued counterparts, this paper treats the networks holistically. We propose a novel adequate condition via the fixed point theorem to achieve mean-square stability, and the obtained results also hold for real, complex and quaternion stochastic neural networks. Lastly, the effectiveness of the proposed criterion is illustrated with two numerical simulation examples.