New LMI-based conditions for global exponential stability to a class of Cohen–Grossberg BAM networks with delays

This paper discusses global exponential stability of equilibrium point for a class of Cohen–Grossberg BAM neural networks with delays. Under the assumptions that the activation functions only satisfy global Lipschitz conditions and the behaved functions only satisfy sign conditions, by applying the linear matrix inequality (LMI) method, degree theory and some inequality technique, a novel LMI-based sufficient condition is established for global exponential stability of the concerned neural networks. In our result, the assumption on the activation functions is less conservative than the assumption for monotonicity in Nie and Cao (2009) [28] and the assumption on the behaved functions is also less conservative than the assumption for differentiability in Nie and Cao (2009) [28], Xia (2010) [30], Zhou and Wan (2009) [31] and Zhang et al. (2012) [35].