Linear Prediction Theory: A Mathematical Basis for Adaptive Systems (Springer Series in Information Sciences)

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9783642752087: Linear Prediction Theory: A Mathematical Basis for Adaptive Systems (Springer Series in Information Sciences)
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Lnear prediction theory and the related algorithms have matured to the point where they now form an integral part of many real-world adaptive systems. When it is necessary to extract information from a random process, we are frequently faced with the problem of analyzing and solving special systems of linear equations. In the general case these systems are overdetermined and may be characterized by additional properties, such as update and shift-invariance properties. Usually, one employs exact or approximate least-squares methods to solve the resulting class of linear equations. Mainly during the last decade, researchers in various fields have contributed techniques and nomenclature for this type of least-squares problem. This body of methods now constitutes what we call the theory of linear prediction. The immense interest that it has aroused clearly emerges from recent advances in processor technology, which provide the means to implement linear prediction algorithms, and to operate them in real time. The practical effect is the occurrence of a new class of high-performance adaptive systems for control, communications and system identification applications. This monograph presumes a background in discrete-time digital signal processing, including Z-transforms, and a basic knowledge of discrete-time random processes. One of the difficulties I have en­ countered while writing this book is that many engineers and computer scientists lack knowledge of fundamental mathematics and geometry.

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9783540518716: Linear Prediction Theory: A Mathematical Basis for Adaptive Systems (Springer Series in Information Sciences)

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Book Description Springer. Paperback. Condition: New. 422 pages. Dimensions: 9.0in. x 6.1in. x 0.9in.The theory presented in this book forms the basis of many algorithms for parameter estimation, adaptive system identification, and adaptive filtering. Linear prediction theory has applications in such fields as communications, control, radar and sonar systems, geophysics, estimation of economic processes, and training problems in synthetic neural nets. Emphasis is placed on three main areas. First, the mathematical tools required for the most important linear prediction algorithms are derived in a unified framework. Second, the relationships between different approaches are pointed out, thus allowing the selection of the optimal technique for a particular problem. Third, the material is presented in the context of the latest results of algorithm research, with many references to recent publications in the field. The book is suitable for a graduate course on adaptive signal processing and will be useful for practising engineers faced with the problem of designing systems for operation in time-varying environments. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783642752087

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Book Description Springer-Verlag Berlin and Heidelberg GmbH Co. KG, Germany, 2011. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.Lnear prediction theory and the related algorithms have matured to the point where they now form an integral part of many real-world adaptive systems. When it is necessary to extract information from a random process, we are frequently faced with the problem of analyzing and solving special systems of linear equations. In the general case these systems are overdetermined and may be characterized by additional properties, such as update and shift-invariance properties. Usually, one employs exact or approximate least-squares methods to solve the resulting class of linear equations. Mainly during the last decade, researchers in various fields have contributed techniques and nomenclature for this type of least-squares problem. This body of methods now constitutes what we call the theory of linear prediction. The immense interest that it has aroused clearly emerges from recent advances in processor technology, which provide the means to implement linear prediction algorithms, and to operate them in real time. The practical effect is the occurrence of a new class of high-performance adaptive systems for control, communications and system identification applications. This monograph presumes a background in discrete-time digital signal processing, including Z-transforms, and a basic knowledge of discrete-time random processes. One of the difficulties I have en- countered while writing this book is that many engineers and computer scientists lack knowledge of fundamental mathematics and geometry. Softcover reprint of the original 1st ed. 1990. Seller Inventory # AAV9783642752087

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