Non-Linear Feedback Neural Networks: VLSI Implementations and Applications (Studies in Computational Intelligence, 508, Band 508) - Hardcover

Buch 50 von 538: Studies in Computational Intelligence

Ansari, Mohd. Samar

 
9788132215622: Non-Linear Feedback Neural Networks: VLSI Implementations and Applications (Studies in Computational Intelligence, 508, Band 508)
Hardcover
ISBN 10: 8132215621 ISBN 13: 9788132215622
Verlag: Springer, 2013

Inhaltsangabe

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

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Über die Autorin bzw. den Autor

Dr. Mohammad Samar Ansari is an Assistant Professor of the Department of Electronics Engineering at Aligarh Muslim University, Aligarh, India. Before this he worked at the same university as a Lecturer and Guest Faculty from September 2004. Dr. Ansari also worked with Defense Research Development Organization (DRDO) and Siemens Limited during the years 2001–2003. He obtained PhD in 2012 (thesis title: Neural Circuits for Solving Linear Equations with Extensions for Mathematical Programming), and completed MTech (Electronics Engineering) in 2007 and BTech (Electronics Engineering) in 2001 from the same university. He has published 15 international journal papers and more than 30 international and national conference papers. He is a Life Member of The Institution of Electronics and Telecommunication Engineers (IETE), India.

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This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

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Verlag
Springer
Erscheinungsdatum
2013
Sprache
Englisch
ISBN 10 
8132215621
ISBN 13 
9788132215622
Einband
Gebundene Ausgabe
Anzahl der Seiten
223
Kontakt zum Hersteller
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ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Deutschland

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