An Introduction to IdentificationAdvanced undergraduates and graduate students of electrical, chemical, mechanical, and environmental engineering will appreciate this text for a course in systems identification. In addition to the theoretical basis for mathematical modeling, it covers a variety of tried-and-true identification algorithms and their applications. Moreover, its broad view and fairly modest mathematical level offer readers a quick appraisal of established methods and their limitations. In addition to surveys covering classical methods of identification — including impulse, step, and sine-wave testing — and identification based on correlation function, the text examines least-squares model fitting, statistical properties of estimators, optimal estimation, and Bayes and maximum-likelihood estimators. Other topics include experiment design and choice of model structure as well as model validation. Numerical examples show students how to apply the modeling theories, and a chapter on specialized topics introduces research areas. |
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... matrix method Generalised least - squares Initial condition Integrated random walk Moving average Matrix - fraction ... Ordinary least - squares Probability density function Persistently exciting Pseudo - random binary sequence Power ...
... matrix method Generalised least - squares Initial condition Integrated random walk Moving average Matrix - fraction ... Ordinary least - squares Probability density function Persistently exciting Pseudo - random binary sequence Power ...
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Common terms and phrases
analysis applied approximation Åström asymptotic autocorrelation Automatica b₁ Bayes estimation behaviour bias Chapter coefficients column computed constant convergence correlation covariance Cramér-Rao bound depends deterministic discrete-time dynamics elements equation Example frequency Gaussian gives h₁ ill-conditioning impulse response initial input input-output instrumental variables iteration Kalman filter Laplace transforms linear system linearly Ljung loss function m-sequence m.l. estimate matrix measurements methods minimise minimum-covariance model order model structure noise non-linear non-zero normal matrix o.l.s. estimate observations orthogonal output errors P₁ parameter estimates positive-definite posterior p.d.f. prediction prior p.d.f. problem random variables records recursive regression regressors residuals samples scalar Section self-tuning sequence signal singular-value decomposition Söderström steady-state gain stochastic stochastic approximation System Identification time-invariant time-varying transfer function transfer-function u₁ unbiased estimator uncorrelated updating v₁ values variance vector x₁ y₁ z-transform zero zero-mean