Global stability of neural networks with distributed delays

In this paper, a model describing the dynamics of recurrent neural networks with distributed delays is considered. Some sufficient criteria are derived ensuring the global asymptotic stability of distributed-delay recurrent neural networks with more general signal propagation functions by introducing real parameters p>1, q(ij)>0, and r(jj)>0, i,j=1,...,n, and applying the properties of the M matrix and inequality techniques. We do not assume that the signal propagation functions satisfy the Lipschitz condition and do not require them to be bounded, differentiable, or strictly increasing. Moreover, the symmetry of the connection matrix is also not necessary. These criteria are independent of the delays and possess infinitely adjustable real parameters, which is important in signal processing, especially in moving image treatment and the design of networks.