Applied Neural Networks for Signal Processing

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Cambridge University Press, 1998 - Computers - 367 pages
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The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas. Applied Neural Networks for Signal Processing is the first book to provide a comprehensive introduction to this broad field. It begins by covering the basic principles and models of neural networks in signal processing. The authors then discuss a number of powerful algorithms and architectures for a range of important problems, and describe practical implementation procedures. A key feature of the book is that many carefully designed simulation examples are included to help guide the reader in the development of systems for new applications. The book will be an invaluable reference for scientists and engineers working in communications, control or any other field related to signal processing. It can also be used as a textbook for graduate courses in electrical engineering and computer science.
 

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Contents

II
1
III
5
IV
11
V
16
VI
20
VII
22
VIII
26
IX
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XLI
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XLII
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XLIII
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XLIV
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XLV
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XLVI
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XLVIII
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XV
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XIX
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XX
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XXI
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XXIII
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XXIV
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XXX
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XXXIII
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XXXIV
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XXXV
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XXXVII
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XXXVIII
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XL
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XLIX
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L
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LI
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LIII
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LIV
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LV
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LVI
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LVIII
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LIX
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LXI
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LXXVI
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LXXVII
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LXXVIII
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LXXIX
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Page 333 - Cowan, SA Billings, and PM Grant, "Parallel Recursive Prediction Error Algorithm for Training Layered Neural Networks.

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About the author (1998)

Fa-Long Luo is Chief Scientist of two leading international companies dealing with software-defined radio and wireless multimedia with headquarters in Silicon Valley, California. He has 27 years of research and industrial experience in multimedia, communication and broadcasting with real-time implementation, applications and standardizations. He has authored two books, more than 100 technical papers and 18 patents in these and closely related fields.

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