Download e-book for kindle: Advanced Process Identification & Control by Enso Ikonen

By Enso Ikonen

ISBN-10: 082470648X

ISBN-13: 9780824706487

Complex strategy id & keep an eye on КНИГИ ;НАУКА и УЧЕБА Автор: Kaddour NajimНазвание: complicated strategy id & ControlИздательство: CRC PressГод: 2001Страниц: 632Формат: PDFISBN-10: 082470648XЯзык: АнглийскийРазмер: 11.64 MBPresents the newest ideas in complicated technique modeling, identity, prediction, and parameter estimation for the implementation and research of business structures. obtain replicate zero 1 2 three four five

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8927 for the bi~ h0. Nem~k 1 (Co~anee matrix) ~or ~ = 1 we obtain Therefore, in ~he frameworkof p~ame~er~timation, the ma~rNP = [~r~] -~ is called the error cov~iance matrN. TLFeBOOK 28 CHAPTER 2. 3 Recursive LINEAR REGRESSION LS method The least squares method provides an estimate for the model parameters, based on a set of observations. Consider the situation whenthe observation pairs are obtained one-by-one from the process, and that we would like to update the parameter estimate whenevernew information becomesavailable.

5 Kalman filter In the Bayesian approach to the parameter estimation problem, the parameter itself is thought of as a randomvariable. Based on the observations of other randomvariables that are correlated with the parameter, we mayinfer information about its value. The Kalmanfilter is developed in such a framework. The unobservable state vector is assumed to be correlated with the output of a system. So, based on the observations of the output, the value of the state vector can be estimated. In what follows, the Kalmanfilter is first introduced for state estimation.

It desirable to predict {x (k)} from the measurementsof {y (k)}. The Kalmanfilter can be derived in a numberof ways. In what follows, the meansquare error approach for the Kalmanpredictor is considered [41]. Wethen proceed by giving the algorithm for the Kalmanfilter (the proof for the filter case is omittedas it is lengthy). 7Kroneckerdelta function is given by ifi=j otherwise TLFeBOOK 40 CHAPTER 2. 137) which consists of two terms: a prediction based on the system model and the previous estimate, and a correction term from the difference betweenthe measuredoutput and the output predicted using the system model.

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Advanced Process Identification & Control by Enso Ikonen

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