| 期刊全稱 | Automatic Autocorrelation and Spectral Analysis |
| 影響因子2023 | Piet M. T. Broersen |
| 視頻video | http://file.papertrans.cn/167/166398/166398.mp4 |
| 發(fā)行地址 | Shows the reader which spectral methods (algorithms) are useful in practice.Demonstrates the clear advantages of using parametric rather than non-parametric models for spectral analysis.Provides the r |
| 圖書封面 |  |
| 影響因子 | ."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.. |
| Pindex | Textbook 2006 |
|Archiver|手機版|小黑屋|
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