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- W2295097481 abstract "The least-squares estimates of the parameters of a transfer operator or difference equation model are biased and not consistent, and the model fit in the frequency domain is not always good because the high frequencies are over-emphasized. This chapter discusses various ways of modifying the least-squares method. For an open-loop experiment, the least-squares method can be modified by following methods: the Steiglitz–McBride method, the output-error method, and the instrumental variable method. For closed-loop data, the prediction error method is used. For evaluating a model order or structure that is the most accurate for its use (in control), two choices need to be made: (1) error criterion and (2) data set on which the criterion is evaluated. The goal of model validation is to check if the identified model is good enough for use in control. If the answer is negative, the validation step provides the necessary information for a possible remedy. The chapter describes the standard methods for model validation." @default.
- W2295097481 created "2016-06-24" @default.
- W2295097481 creator A5005139254 @default.
- W2295097481 date "2001-01-01" @default.
- W2295097481 modified "2023-09-27" @default.
- W2295097481 title "Extensions of the Least-Squares Method" @default.
- W2295097481 doi "https://doi.org/10.1016/b978-008043985-3/50007-7" @default.
- W2295097481 hasPublicationYear "2001" @default.
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