Strengthened stability analysis of discrete-time Lurie systems involving ReLU neural networks

This paper addresses the stability analysis of a discrete-time (DT) Lurie system featuring a static repeated ReLU nonlinearity. Such systems often arise in the analysis of recurrent neural networks and other neural feedback loops. Custom quadratic constraints, satisfied by the repeated ReLU, are employed to strengthen the standard Circle and Popov Criteria for this specific Lurie system. The criteria can be expressed as a set of linear matrix inequalities (LMIs) with less restrictive conditions on the matrix variables. It is further shown that if the Lurie system under consideration has a unique equilibrium point at the origin, then this equilibrium point is in fact globally stable or unstable, meaning that local stability analysis will provide no additional benefit. Numerical examples demonstrate that the strengthened criteria achieve a desirable balance between reduced conservatism and complexity when compared to existing criteria.