System identification: theory for the user
The field's leading text, now completely updated. Modeling dynamical systems -- theory, methodology, and applications. Lennart Ljung's "System Identification: Theory for the User" is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling. The book contains many new computer-based examples designed for Ljung's market-leading software, System Identification Toolbox for MATLAB. Ljung combines careful mathematics, a practical understanding of real-world applications, and extensive exercises. He introduces both black-box and tailor-made models of linear as well as non-linear systems, and he describes principles, properties, and algorithms for a variety of identification techniques: Nonparametric time-domain and frequency-domain methods. Parameter estimation methods in a general prediction error setting. Frequency domain data and frequency domain interpretations. Asymptotic analysis of parameter estimates. Linear regressions, iterative search methods, and other ways to compute estimates. Recursive (adaptive) estimation techniques. Ljung also presents detailed coverage of the key issues that can make or break system identification projects, such as defining objectives, designing experiments, controlling the bias distribution of transfer-function estimates, and carefully validating the resulting models. The first edition of System Identification has been thefield's most widely cited reference for over a decade. This new edition will be the new text of choice for anyone concerned with system identification theory and practice.
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systems and models
SIMULATION PREDICTION AND CONTROL
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algorithm analogous applied approach approximation assume assumption Astrom asymptotic basic black-box Bode plot Chapter choice coefficients computed Consider convergence correlation corresponding covariance function covariance matrix data set defined definition denotes described design variables determine deterministic discussed distribution disturbances dynamics equation ETFE example experiment expression Figure filter formal frequency frequency-domain Gaussian given gives Hence input input-output Kalman filter least-squares Lemma likelihood function linear model linear regression linear system Ljung measured minimization model order model set model structure multivariable noise model nonlinear norm obtained optimal parameter estimation periodogram polynomials prediction errors prediction-error method predictor prefilter problem procedure properties quadratic random variables recursive regressors result sampling interval scalar Section Show signal simulation Soderstrom spectral analysis spectrum stable state-space model stochastic process Stoica Suppose system identification techniques Theorem tion transfer function true system typically white noise zero mean