Titlebook: System Identification; An Introduction Karel J. Keesman Textbook 2011 Springer-Verlag London Limited 2011 Control Applications.Control Engi

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Dynamic Systems Identificationparticular, the structure of the noise model appear to be of crucial importance for specific applications and the estimation methods to be used. In this chapter, it is stressed that both the linear and nonlinear model structures can be formulated in terms of (nonlinear) regression equations, which a

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Time-varying Static Systems Identifications follows. On the basis of common prior knowledge, the model parameters in the linear regression models are considered as constant. Subsequently, the experimental data, using recursive estimation techniques, will tell how the estimates of the parameters vary with time. This idea can be easily extend

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Time-varying Dynamic Systems Identificationman filtering and observer-based methods. And, again it will be applied to both the linear and nonlinear cases. The theory is illustrated by real-world examples, with most often a biological component in it, as these cases often show a time-varying behavior due to adaptation of the (micro)organisms.

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Dynamic Systems Identificationes ., ., ., and . in a discrete-time, linear state-space model formulation, to the identification of discrete-time linear parameter-varying models of nonlinear or time-varying systems, to the use of orthogonal basis functions for efficient calculation, and to closed-loop identification in LTI control system configurations.

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