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Identification of Time-Varying Processes
 
 
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Identification of Time-Varying Processes [Hardcover]

Maciej Niedzwiecki (Author)

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Book Description

0471986291 978-0471986294 August 30, 2000 1
Identification of Time-Varying Processes offers a comprehensive treatment of the key issue in adaptive systems: tracking of time-varying system parameters. Time-varying identification techniques facilitate many challenging applications in different areas including telecommunications (channel equalization, predictive coding of signals, adaptive noise reduction and echo cancellation) and automatic control (adaptive control and failure detection). The processes also assist signal processing in areas such as adaptive noise reduction, prediction of time series, restoration of archive audio recordings and spectrum estimation. Includes:
* All three major approaches to time-varying identification: local estimation, the basis functions approach and the method based on Kalman filtering/smoothing.
* Analysis and comparison of tracking capabilities of different time-varying identification schemes.
* Discussion of all aspects of time-varying identification such as assessment of the estimation memory, estimation bandwidth and numerical stability of different identification algorithms and optimization of adaptive filters.
* Presentation of selected practical applications of time-varying process identification.
Essential reading for adaptive signal processing engineers, researchers, lecturers and senior electrical engineering and computer science students in telecommunications and signal processing.

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Editorial Reviews

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"...a comprehensive treatment...well-written and successful in combining mathematics and practical understanding of real world applications..." (Automatica, No.38, 2002)

From the Back Cover

Identification of Time-Varying Processes offers a comprehensive treatment of the key issue in adaptive systems: tracking of time-varying system parameters. Time-varying identification techniques facilitate many challenging applications in different areas including telecommunications (channel equalization, predictive coding of signals, adaptive noise reduction and echo cancellation) and automatic control (adaptive control and failure detection). The processes also assist signal processing in areas such as adaptive noise reduction, prediction of time series, restoration of archive audio recordings and spectrum estimation. Includes:
* All three major approaches to time-varying identification: local estimation, the basis functions approach and the method based on Kalman filtering/smoothing.
* Analysis and comparison of tracking capabilities of different time-varying identification schemes.
* Discussion of all aspects of time-varying identification such as assessment of the estimation memory, estimation bandwidth and numerical stability of different identification algorithms and optimization of adaptive filters.
* Presentation of selected practical applications of time-varying process identification.
Essential reading for adaptive signal processing engineers, researchers, lecturers and senior electrical engineering and computer science students in telecommunications and signal processing.

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Inside This Book (learn more)
First Sentence:
When building a mathematical model of a physical process (a signal or a system) one can proceed in two different ways. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
continuous parameter changes, parameter tracking error, system nonstationarity, parameter tracking algorithms, equivalent memory span, slow adaptation condition, excess prediction error, process time variation, sliding window least squares, show identification results, finite impulse response systems, weighted basis functions, exponentially weighted least squares, process nonstationarity, estimators characterized, vertical bars show standard deviation, estimation bandwidth, estimation memory, noise shaping filter, mean square prediction error, parameter trajectory, stationary excitation, regression vector, estimation delay, regression matrix
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Jerzy Majkowski, Remark Note
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