A neural network approach is proposed for the characterization of twisted wire bundles multiconductor transmission lines. The neural network is suitably trained with few cross section configurations to learn the behavior of the per unit length parameters of the nonuniform bundle. The procedure allows a fast characterization of the nonuniform bundles multiconductor transmission lines.

For nonuniform exponentially bounded evolution families defined on Banach spaces, we introduce a class of Banach function spaces, whose norms are uniquely determined by the nonuniform behavior of the corresponding evolution family. We generalize the classical theorem of Datko on these spaces.

A joint characterization of reachability (controllability) and observability (constructibility) for linear SISO nonuniformly sampled discrete systems is presented. The work generalizes to the nonuniform sampling the criterion known for the uniform sampling. Emphasis is on the nonuniform sampling sequence, which is believed to be an additional element for analysis and handling of discrete systems.