Nonlinear fractional distributed Halanay inequality and application to neural network systems

The standard first order distributed Halanay inequality is generalized in more than one direction. We prove a fractional nonlinear version of this inequality for a large class of kernels which are not necessarily exponentially decaying to zero. This result is used to prove Mittag-Leffler stability of a Hopfiled neural network system with not necessarily globally Lipschitz continuous activation functions. Two classes of important admissible kernels and an example are provided to illustrate our findings.