A comprehensive framework for training stable and passive multivariate behavioral models

We present a theoretical framework and related algorithms for the construction of behavioral models of linear or linearized devices. Unlike competing approaches, the proposed method is robust and guarantees theoretically the uniform stability and passivity of the models in a multivariate setting, where the model behavior depends not only on time or frequency but also on a number of design/stochastic parameters. Various examples demonstrate the high accuracy and reliability of proposed framework.