Identification of Nonlinear Stochastic Grey Box Models: Theory, Implementation, and Experiences

Abstract Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. If this leads to nonlinear stochastic state space models, there is a question as to whether the complexity of analysis and software implementation is justifiable in terms of improved control and/or prediction. The paper covers the following three issues. Firstly, the theoretical foundations for solving the problem are reviewed. Secondly, the implementation of a general purpose grey box identification software is reported. And thirdly, with help of two industrial applications, it is demonstrated that the complexity of the method can indeed pay off in terms of simplifying control (case study 1) or in terms of improved prediction and parameter accuracy (case study 2).