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Thursday, September 2, 2021 | History

6 edition of Neural networks for signal processing found in the catalog.

Neural networks for signal processing

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  • 37 Currently reading

Published by Prentice Hall in Englewood Cliffs, NJ .
Written in English

    Subjects:
  • Signal processing -- Digital techniques -- Data processing.,
  • Neural networks (Computer science)

  • Edition Notes

    Includes bibliographical references and index.

    StatementBart Kosko, editor.
    ContributionsKosko, Bart.
    Classifications
    LC ClassificationsTK5102.5 .N44 1992
    The Physical Object
    Paginationxv, 399 p. :
    Number of Pages399
    ID Numbers
    Open LibraryOL1542254M
    ISBN 10013617390X
    LC Control Number91021081


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The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs provide solutions in real time, and allow the visualization and development of new techniques and architectures.

The book 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 go on to describe practical implementation procedures.

This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects.

This peer reviewed book presents some recent advances and surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing. It chapters are prepared by some reputed researchers and practitioners around the globe.

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Publication date Topics Signal processing, Neural networks (Computer science) Publisher Englewood Cliffs, NJ.   Keywords: Neural networks, Signal processing.

The "Handbook of Neural Network Signal Processing" is a clearly written and comprehensive reference text, which provides a practical examination of neural networks and their applications.

The book comprises 13 chapters addressing three focal areas. The first five chapters provide in-depth surveys of Author: Jon Rigelsford.

Applied Neural Networks for Signal Processing book. Read reviews from worlds largest community for readers. The use of neural networks in signal process. The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas.

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A topical introduction on the abili. Books Catalogue (Individuals) English Language Learning; Education; Bibles; Digital Products; information theory and signal processing; Applied Neural Networks for Signal Processing. Authors: Fa-Long Luo; Rolf Unbehauen, Friedrich-Alexander-Universität Erlangen-Nürnberg.

This will be an introductory graduate level course in neural networks for signal processing. The course starts with a motivation of how the human brain is inspirational to building artificial neural networks.

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About this product. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

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.

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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. [Show full abstract] builds an intelligent analysis system for signal processing tasks based on the LSTM recurrent neural network algorithm.

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A noted speaker, editor, and author, Dr. Akay has spent several years conducting research in the areas of fuzzy neural networks and signal processing, wavelet transform, and detection and estimation theory.

Abstract. The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks.

Convolutional neural networks (CNNs) represent an interesting method for adaptive image processing, and form a link between general feed-forward neural networks and adaptive filters.

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His present research interests include also adaptive filters, audio processing, neural networks for signal processing, blind signal processing.

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