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Titlebook: Nonlinear Time Series and Signal Processing; R. R. Mohler Book 1988 Springer-Verlag Berlin Heidelberg 1988 acoustics.control.detection.dyn

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發(fā)表于 2025-3-21 19:45:27 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Nonlinear Time Series and Signal Processing
編輯R. R. Mohler
視頻videohttp://file.papertrans.cn/668/667734/667734.mp4
叢書名稱Lecture Notes in Control and Information Sciences
圖書封面Titlebook: Nonlinear Time Series and Signal Processing;  R. R. Mohler Book 1988 Springer-Verlag Berlin Heidelberg 1988 acoustics.control.detection.dyn
描述This monograph provides a sample of relevant new results on dynamical nonlinear statistical modeling and estimation which forms a basis for more effective signal processing, decision and control. While the research literature is rich in linear Gaussian methodologies, new contributions to the most relevant area of nonlinear and non-Gaussian processes have been scarce. Among the significant areas of application for which such methodologies are needed are: economics, biology, immunology, underwater acoustics, electric power generation, chemical process control, and variable structure systems in general. The latter include adaptive, intelligent, and decomposing mathematical structures or processes. The volume includes ten research papers on theory, computational methods, and applications. Topics include filtering with application to inertial navigation, structural-change detection, bilinear time-series models, bispectral estimation, threshold models, catastrophic models and a generalized eigenstructure method.
出版日期Book 1988
關(guān)鍵詞acoustics; control; detection; dynamical systems; filtering; process control; signal processing
版次1
doihttps://doi.org/10.1007/BFb0044270
isbn_softcover978-3-540-18861-2
isbn_ebook978-3-540-38837-1Series ISSN 0170-8643 Series E-ISSN 1610-7411
issn_series 0170-8643
copyrightSpringer-Verlag Berlin Heidelberg 1988
The information of publication is updating

書目名稱Nonlinear Time Series and Signal Processing影響因子(影響力)




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沙發(fā)
發(fā)表于 2025-3-21 20:27:56 | 只看該作者
Spectral and bispectral methods for the analysis of nonlinear (non Gaussian) time series signals,eries model can be a useful alternative when the time series is nonlinear. We illustrate the estimation methods with many simulated and one real data set. The real data is concerned with the magnetic reversals.
板凳
發(fā)表于 2025-3-22 01:51:54 | 只看該作者
Book 1988tive signal processing, decision and control. While the research literature is rich in linear Gaussian methodologies, new contributions to the most relevant area of nonlinear and non-Gaussian processes have been scarce. Among the significant areas of application for which such methodologies are need
地板
發(fā)表于 2025-3-22 07:19:11 | 只看該作者
Bivariate bilinear models and their specification,bivariate bilinear models by looking at these tables. It may also be possible to distinguish the lags of the particular model by carefully looking at the table..It may be more interesting to look at the third order cross moment table for a few more general bivariate bilinear models.
5#
發(fā)表于 2025-3-22 09:02:34 | 只看該作者
Nonlinear processing with Mth-order signals,ls. The second order is readily available to any signal processing technique with a linear observation equation. On the other hand, to be able to use higher-order eigenstructures, though Gaussianity is not enforced, powerful simplifications occur if the odd moments of the noises vanish.
6#
發(fā)表于 2025-3-22 16:21:16 | 只看該作者
0170-8643 bilinear time-series models, bispectral estimation, threshold models, catastrophic models and a generalized eigenstructure method.978-3-540-18861-2978-3-540-38837-1Series ISSN 0170-8643 Series E-ISSN 1610-7411
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發(fā)表于 2025-3-22 21:01:51 | 只看該作者
On the application of kalman filtering to correct errors due to vertical deflections in inertial na
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發(fā)表于 2025-3-22 21:34:02 | 只看該作者
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發(fā)表于 2025-3-23 05:12:52 | 只看該作者
Nonlinear time series analysis for dynamical systems of catastrophe type,
10#
發(fā)表于 2025-3-23 08:20:10 | 只看該作者
Book 1988ory, computational methods, and applications. Topics include filtering with application to inertial navigation, structural-change detection, bilinear time-series models, bispectral estimation, threshold models, catastrophic models and a generalized eigenstructure method.
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