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Noise signalsmodelling and analyses /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Noise signalsby Vitalii Babak ... [et al.].
其他題名:	modelling and analyses /
其他作者:	Babak, Vitalii.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	x, 222 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Random noise theory.
電子資源:	https://doi.org/10.1007/978-3-031-71093-3
ISBN:	9783031710933$q(electronic bk.)

Noise signalsmodelling and analyses /
Noise signalsmodelling and analyses /[electronic resource] :by Vitalii Babak ... [et al.]. - Cham :Springer Nature Switzerland :2025. - x, 222 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v. 5672198-4190 ;. - Studies in systems, decision and control ;v.3..

Chapter 1. Problems of Noise Signals Research -- Chapter 2. Linear Models of Stochastic Noise Signals -- Chapter 3. Periodic Models of Noise Signals -- Chapter 4. Method of Envelope and Phase in the Tasks of Identification of Narrowband Noise Signals -- Chapter 5. Identification of Vibration Noise Signals of Electric Power Facilities -- Chapter 6. Examples of Stochastic Noise Signals Identification -- Chapter 7. Identification of Air Pollution Sources.

The book meticulously details a constructive mathematical model of a stochastic noise process, specifically a linear random process and its characteristics. Theoretical reasoning on the relationship between random processes with independent increments and those with independent values, known as random processes of white noise, is provided. The model of a linear random process serves as a mathematical representation of colored noises in various hues. Characteristics of both non-stationary and stationary linear random processes are elucidated, with emphasis on their ergodic properties, crucial for practical applications. The study also encompasses the vector linear random process, portraying a model of multi-channel noise signals. A novel contribution to the theory of random functions is the development of a constructive model of a conditional linear random process. This involves determining its distribution laws in the form of a characteristic function and relevant statistical characteristics, which can serve as potential indicators for identifying stochastic noise processes. The book revisits research on periodic stochastic models, examining cyclic, rhythmic, natural, and artificial phenomena, processes, and signals. A comprehensive analysis of the linear periodic random process is conducted, and the identification characteristics of periodic models of stochastic noise signals are explored. Significant attention is directed toward employing contour and phase methods as a theoretical foundation for addressing narrow-band noise signal identification challenges.

ISBN: 9783031710933$q(electronic bk.)

Standard No.: 10.1007/978-3-031-71093-3doiSubjects--Topical Terms:

224681
Random noise theory.

LC Class. No.: Q380

Dewey Class. No.: 620.23

Noise signalsmodelling and analyses /
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520 $a The book meticulously details a constructive mathematical model of a stochastic noise process, specifically a linear random process and its characteristics. Theoretical reasoning on the relationship between random processes with independent increments and those with independent values, known as random processes of white noise, is provided. The model of a linear random process serves as a mathematical representation of colored noises in various hues. Characteristics of both non-stationary and stationary linear random processes are elucidated, with emphasis on their ergodic properties, crucial for practical applications. The study also encompasses the vector linear random process, portraying a model of multi-channel noise signals. A novel contribution to the theory of random functions is the development of a constructive model of a conditional linear random process. This involves determining its distribution laws in the form of a characteristic function and relevant statistical characteristics, which can serve as potential indicators for identifying stochastic noise processes. The book revisits research on periodic stochastic models, examining cyclic, rhythmic, natural, and artificial phenomena, processes, and signals. A comprehensive analysis of the linear periodic random process is conducted, and the identification characteristics of periodic models of stochastic noise signals are explored. Significant attention is directed toward employing contour and phase methods as a theoretical foundation for addressing narrow-band noise signal identification challenges.
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