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- Author: Lennart Ljung
- Publisher: Pearson
- ISBN-10: 0136566952
- ISBN-13: 9780136566953
- Format: 18.5 x 24.3 x 3 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
This is a complete, coherent description of the theory, methodology and practice of System Identification. The completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and these key non-linear black box methods: neural networks, wavelet transforms, neuro-fuzzy modeling and hinging hyperplanes.KEY TOPICS: Leader in the field Lennart Ljung introduces systems and models, time-invariant linear systems, time-varying and nonlinear systems. He presents several approaches to system identification, including nonparametric time- and frequency-domain methods; parameter estimation; convergence and consistency; asymptotic distribution of parameter estimates; linear regressions, iterative search and recursive estimation. He also presents detailed coverage of key issues that can make or break system identification projects: defining objectives, designing experiments, selecting criteria, and controlling the bias distribution of transfer-function estimates.MARKET: For all engineering and control systems professionals, faculty and students.
EXTRA 10 % discount with code: EXTRA
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- Author: Lennart Ljung
- Publisher: Pearson
- ISBN-10: 0136566952
- ISBN-13: 9780136566953
- Format: 18.5 x 24.3 x 3 cm, hardcover
- Language: English English
This is a complete, coherent description of the theory, methodology and practice of System Identification. The completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and these key non-linear black box methods: neural networks, wavelet transforms, neuro-fuzzy modeling and hinging hyperplanes.KEY TOPICS: Leader in the field Lennart Ljung introduces systems and models, time-invariant linear systems, time-varying and nonlinear systems. He presents several approaches to system identification, including nonparametric time- and frequency-domain methods; parameter estimation; convergence and consistency; asymptotic distribution of parameter estimates; linear regressions, iterative search and recursive estimation. He also presents detailed coverage of key issues that can make or break system identification projects: defining objectives, designing experiments, selecting criteria, and controlling the bias distribution of transfer-function estimates.MARKET: For all engineering and control systems professionals, faculty and students.
Reviews