Information Theoretic Identification Criteria: Approaches and Alternatives

The aim of the paper is to present a conceptual approach to identification of nonlinear stochastic systems based on information measures of dependence. In the paper, an identification problem statement using the information criterion under rather general conditions is proposed. It is based on a parameterized description of the system model under study combined with a corresponding method of estimation of the mutual information of the system’s and model’s output variables. Such a problem statement leads finally to a problem of the finite dimensional optimization. As a result, a constructive procedure of the model parameter identification is derived. It possesses a high level of generality and does not involve unreal a priori assumptions degenerating the entity of the initial identification problem statement like those ones presented in some referenced literature sources and revised in the present paper. Copyright © 2005 IFAC