On The Consistency of Prediction Error Identification Methods

Publisher Summary The problem of identification is to determine a model that describes input–output data obtained from a certain system. In this chapter, strong consistency for general prediction error methods, including the maximum-likelihood (ML) method is considered. The results are valid for general process models: linear and nonlinear. An error identification method is discussed in the chapter along with a general model for stochastic dynamic systems. Different identifiability concepts are also introduced, where a procedure to prove consistency is outlined. C onsistency is shown for a general system structure, as well as for linear systems. The application of the results to linear time-invariant systems is also discussed in the chapter.

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