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Titlebook: Identification of Continuous-Time Systems; Methodology and Comp N. K. Sinha,G. P. Rao Book 1991 Springer Science+Business Media Dordrecht 1

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Recursive block pulse function method,ding to the differential equation models of single-input, single-output linear systems, recursive algorithms developed in discrete-time model identification can be applied directly to estimate the parameters of the original differential equations without much modification. The recursive block pulse
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Continuous model identification via orthogonal polynomials,lined in this Chapter. In this process, the system’s initial conditions are also determined. Lumped and distributed parameter system models which are linear and time-invariant are considered for our study. The estimates of parameters and initial conditions obtained by orthogonal polynomials are comp
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Identification, Estimation and Control of Continuous-Time Systems Described by Delta Operator Modelan be described by delta (δ) operator models with constant or time-variable parameters. It shows how recursive refined instrumental variable estimation algorithms can prove effective both in off-line model identification and estimation, and in the implementation of self-tuning or self-adaptive True
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Identification of multivariable continuous-time systems,) initial conditions and measurement offset or bias. The system model structure used is a minimal order input-output representation. Differentiation of measured data is avoided by means of either multiple lowpass filtering or multiple finite time integration. Equations in unknown parameters are set
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SVD-based subspace methods for multivariable continuous-time systems identification, are computed directly from input/output data. These state space identification methods are viewed as the better alternatives to polynomial model identification, owing to the better numerical conditioning associated with state space models, especially for high-order multivariable systems..In this co
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