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hardcover. Zustand: Wie neu. 310 Seiten; 9781846283284.1 Gewicht in Gramm: 1.

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Zustand: New. Shows the reader which spectral methods (algorithms) are useful in practiceDemonstrates the clear advantages of using parametric rather than non-parametric models for spectral analysisProvides the reader with detailed assistance in using th.

Taschenbuch. Zustand: Neu. Automatic Autocorrelation and Spectral Analysis | Petrus M. T. Broersen | Taschenbuch | xii | Englisch | 2010 | Springer | EAN 9781849965811 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Automatic Autocorrelation and Spectral Analysis' gives random data a language to communicate the information they contain objectively. It takes advantage of greater computing power and robust algorithms to produce enough candidate models of a given group of data to be sure of providing a suitable one. Improved order selection guarantees that one of the best (often the best) will be selected automatically. Written for graduate signal processing students and for researchers and engineers using time series analysis for applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers:- tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models;- extensive support for the MATLAB® ARMAsel toolbox;- applications showing the methods in action;- appropriate mathematics for students to apply the methods with references for those who wish to develop them further.

Zustand: New. In.

Buch. Zustand: Neu. Neuware - Automatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively. It takes advantage of greater computing power and robust algorithms to produce enough candidate models of a given group of data to be sure of providing a suitable one. Improved order selection guarantees that one of the best (often the best) will be selected automatically. Written for graduate signal processing students and for researchers and engineers using time series analysis for applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers: tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models; extensive support for the MATLAB ARMAsel toolbox; applications showing the methods in action; appropriate mathematics for students to apply the methods with references for those who wish to develop them further.