Nonlinear FIR Identification with Model Order Reduction Steiglitz-McBride⁎

• In system identification, many structures and approaches have been proposed to deal with systems with non-linear behavior. When applicable, the prediction error method, analogously to the linear case, requires minimizing a cost function that is non-convex in general. The issue with non-convexity is more problematic for non-linear models, not only due to the increased complexity of the model, but also because methods to provide consistent initialization points may not be available for many model structures. In this paper, we consider a non-linear rational finite impulse response model. We observe how the prediction error method requires minimizing a non-convex cost function, and propose a three-step least-squares algorithm as an alternative procedure. This procedure is an extension of the Model Order Reduction Steiglitz-McBride method, which is asymptotically efficient in open loop for linear models. We perform a simulation study to illustrate the applicability and performance of the method, which suggests that it is asymptotically efficient. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

least-squares identification

non-linear

System identification

Cost functions

Identification (control systems)

Impulse response

Model structures

Religious buildings

Asymptotically efficient

Finite impulse response model

Least squares algorithm

Least squares identification

Non linear

Nonconvex cost functions

Prediction error method

Steiglitz-Mcbride method

Least squares approximations

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)