Titlebook: Identification of Dynamic Systems; An Introduction with Rolf Isermann,Marco Münchhof Textbook 20111st edition Springer-Verlag Berlin Heidel

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Mathematical Models of Linear Dynamic Systems and Stochastic Signals,dels of linear, time-invariant SISO processes as well as stochastic signals shall shortly be presented in the following. It is assumed that the reader is already familiar with timeand frequency domain based models and methods.

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Correlation Analysis with Discrete Time Models more closely in this chapter. This case is required for the implementation on digital computers. The difference in the treatment of continuous-time and discrete-time signals is rather small as it only affects the calculation of the correlation functions, where basically the continuous-time integrat

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Least Squares Parameter Estimation for Dynamic Processestime already. The application of the method of least squares to the identification of dynamic processes has been tackled with much later in time. First works on the parameter estimation of AR models have been reported in the analysis of time series of economic data (Koopmans, 1937; Mann and Wald, 19

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Parameter Estimation in Closed-Looptegrated and by no means detachable part of the system. For technical systems, e.g. in the area of adaptive control systems, the process model must also be updated while the system is under closed-loop control. Furthermore, integral acting processes can typically only be operated reliably in closed-