Evaluation of estimated hammerstein models via normalized projection misalignment of linear and nonlinear subsystems

In linear system identification, the coexistence of parameter-misadjustment and output-error metrics has turned out very practical and their relation is well understood. In nonlinear system identification, however, such tools for performance evaluation are far less developed and each nonlinear type may need its own treatment. This paper focuses on the Hammerstein model as an instance of nonlinear systems. Irrespective of particular identification algorithms, we generalize the framework of parameter-and output-based performance metrics known from linear systems. An ambiguity in system parameters is resolved via the projection misalignment technique.