Generalized cellular neural networks represented in the NLq framework

The aim of this paper is to show that discrete time Generalized Cellular Neural Networks, with feedforward, feedback or cascade interconnections between CNNs can be represented as NL_qs. NL _qs are nonlinear systems in state space form with the typical feature of having a number of q layers with alternating linear and nonlinear operators that satisfy a sector condition. It can be shown that many systems and problems arising in neural networks, systems and control are special cases of NL_qs. Sufficient conditions for global asymptotic stability and dissipativity with finite L₂-gain are available. For q=1 the criteria are closely related to known results in H_∞ and μ control theory