Automatic Autocorrelation and Spectral Analysis

Petrus M. T. Broersen

ISBN 10: 1849965811 / ISBN 13: 9781849965811

Published by Springer London, Springer London Okt 2010, 2010

About this Item

This item is printed on demand - Print on Demand Titel. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 312 pp. Englisch.

Seller Inventory # 9781849965811

Bibliographic Details

• Title: Automatic Autocorrelation and Spectral Analysis
• Publisher: Springer London, Springer London Okt 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 1849965811
• ISBN 13: 9781849965811
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Spectral analysis requires subjective decisions which influence the final estimate and mean that different analysts can obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that is only acceptable if it is close to the best attainable accuracy for most types of stationary data. This book describes a method which fulfils the above near-optimal-solution criterion, taking advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data.

About the Author

Piet M.T. Broersen received the Ph.D. degree in 1976, from the Delft University of Technology in the Netherlands.

He is currently with the Department of Multi-scale Physics at TU Delft. His main research interest is in automatic identification on statistical grounds. He has developed a practical solution for the spectral and autocorrelation analysis of stochastic data by the automatic selection of a suitable order and type for a time series model of the data.