Título: |
Non-Linear Feedback Neural Networks VLSI Implementations and Applications by Mohd. Samar Ansari. |
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Autor:
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Ansari, Mohd. Samar.
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Editado por: |
Springer India :;Imprint: Springer,
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Año de publicación: |
2014.
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Lugar de publicación: |
New Delhi :
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Descripción física: |
xxii, 201 p. : il. |
ISBN: |
9788132215639
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Colección: |
Studies in Computational Intelligence,
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Materias: |
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Instrumentation. |
Mathematical Models of Cognitive Processes and Neural Networks. |
Circuits and Systems. |
Computational Intelligence. |
Engineering. |
Electronic circuits. |
Microelectronics. |
Electronics. |
Neural networks (Computer science). |
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Notas: |
Introduction -- Background -- Voltage-mode Neural Network for the Solution of Linear Equations -- Mixed-mode Neural Circuit for Solving Linear Equations -- Non-Linear Feedback Neural Circuits for Linear and Quadratic Programming -- OTA-based Implementations of Mixed-mode Neural Circuits -- Appendix A: Mixed-mode Neural Network for Graph Colouring -- Appendix B: Mixed-mode Neural Network for Ranking. |
Sumario: |
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. |
URL: |
http://dx.doi.org/10.1007/978-81-322-1563-9
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