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Titlebook: Random Signals Estimation and Identification; Analysis and Applica Nirode Mohanty Book 1986 Van Nostrand Reinhold Company Inc. 1986 acousti

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書目名稱Random Signals Estimation and Identification
副標(biāo)題Analysis and Applica
編輯Nirode Mohanty
視頻videohttp://file.papertrans.cn/822/821084/821084.mp4
圖書封面Titlebook: Random Signals Estimation and Identification; Analysis and Applica Nirode Mohanty Book 1986 Van Nostrand Reinhold Company Inc. 1986 acousti
描述The techniques used for the extraction of information from received or ob- served signals are applicable in many diverse areas such as radar, sonar, communications, geophysics, remote sensing, acoustics, meteorology, med- ical imaging systems, and electronics warfare. The received signal is usually disturbed by thermal, electrical, atmospheric, channel, or intentional inter- ferences. The received signal cannot be predicted deterministically, so that statistical methods are needed to describe the signal. In general, therefore, any received signal is analyzed as a random signal or process. The purpose of this book is to provide an elementary introduction to random signal analysis, estimation, filtering, and identification. The emphasis of the book is on the computational aspects as well as presentation of com- mon analytical tools for systems involving random signals. The book covers random processes, stationary signals, spectral analysis, estimation, optimiz- ation, detection, spectrum estimation, prediction, filtering, and identification. The book is addressed to practicing engineers and scientists. It can be used as a text for courses in the areas of random processes, estimation
出版日期Book 1986
關(guān)鍵詞acoustics; algebra; algorithms; communication; correlation; detection; electronics; filtering; optimization;
版次1
doihttps://doi.org/10.1007/978-94-011-7041-3
isbn_softcover978-94-011-7043-7
isbn_ebook978-94-011-7041-3
copyrightVan Nostrand Reinhold Company Inc. 1986
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Prediction, Filtering, and Identification, not be measured directly. We will describe the state space for the dynamical system. The state space method converts the signal to a Markov process. We will be concerned with estimation and identification of Markov processes and system modeUing. We will touch upon controllabihty, observability, and
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efinition, key words, an illustration, applications, a bibliography, and links to related literature..The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. M
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Nirode Mohantyliterature..The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. M978-1-4899-7687-1
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Nirode Mohantyliterature..The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. M978-1-4899-7687-1
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