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Inhaltsangabe
This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.The uniqueness of the book is in the breadth of its coverage over the range of all major artificial neural network approaches and in extensive hands-on case-studies on each and every neural network considered. These detailed case studies include complete program print-outs and results and deal with a range of problems, to illustrate the reader's ability to solve problems ranging from speech recognition, character recognition to control and signal processing problems, all on the basis of following the present text. Another unique aspect of the text is its coverage of important new topics of recurrent (time-cycling) networks and of large memory storage and retrieval problems.The text also attempts to show the reader how he can modify or combine one or more of the neural networks covered, to tailor them to a given problem which does not appear to fit any of the more standard designs, as is very often the case.
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This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.The uniqueness of the book is in the breadth of its coverage over the range of all major artificial neural network approaches and in extensive hands-on case-studies on each and every neural network considered. These detailed case studies include complete program print-outs and results and deal with a range of problems, to illustrate the reader's ability to solve problems ranging from speech recognition, character recognition to control and signal processing problems, all on the basis of following the present text. Another unique aspect of the text is its coverage of important new topics of recurrent (time-cycling) networks and of large memory storage and retrieval problems.The text also attempts to show the reader how he can modify or combine one or more of the neural networks covered, to tailor them to a given problem which does not appear to fit any of the more standard designs, as is very often the case.
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- VerlagWorld Scientific Publishing Co Pte Ltd
- Erscheinungsdatum1997
- ISBN 10 9810225164
- ISBN 13 9789810225162
- EinbandTapa dura
- SpracheEnglisch
- Anzahl der Seiten252
- Kontakt zum HerstellerNicht verfügbar
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Principles of Artificial Neural Networks
Verlag:
WORLD SCIENTIFIC PUB CO (, 1997
ISBN 10: 9810225164
ISBN 13: 9789810225162
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Zustand: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. | Seiten: 238 | Sprache: Englisch | Produktart: Bücher. Bestandsnummer des Verkäufers 1246795/2
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Principles of Artificial Neural Networks
Verlag:
WORLD SCIENTIFIC PUB CO (, 1997
ISBN 10: 9810225164
ISBN 13: 9789810225162
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Hardcover
Anbieter: Buchpark, Trebbin, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: Gut. Zustand: Gut - Gebrauchs- und Lagerspuren. | Seiten: 238 | Sprache: Englisch | Produktart: Bücher. Bestandsnummer des Verkäufers 1246795/3
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PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (Advanced Series in Circuits and Systems)
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World Scientific Pub Co Inc, 1997
ISBN 10: 9810225164
ISBN 13: 9789810225162
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Principles of Artificial Neural Networks
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World Scientific Pub Co Inc, 1997
ISBN 10: 9810225164
ISBN 13: 9789810225162
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Principles of Artificial Neural Networks (Advanced Series in Circuits and Systems)
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World Scientific Pub Co Inc, 1997
ISBN 10: 9810225164
ISBN 13: 9789810225162
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