Intrinsically stable IIR filters and IIR-MLP neural networks for signal processing

This paper presents a new technique to control stability of IIR adaptive filters based on the idea of intrinsically stable operations that makes it possible to continually adapt the coefficients with no need of a stability test or pole projection. The coefficients are adapted in a way that intrinsically assures the poles to be in the unit circle. This makes it possible to use a higher step size (also named learning rate here) potentially improving the fastness of adaptation with respect to methods that employ a bound on the learning rate or methods that simply do not control stability. This method can be applied to various realizations: direct forms, cascade or parallel of second order sections, lattice forms. It can be implemented to adapt a simple IIR adaptive filter or a locally recurrent neural network such as the IIR-MLP