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Non-linear feedback neural networks [electronic resource] : VLSI  implementations and application

Non-linear feedback neural networks [electronic resource] : VLSI implementations and application

자료유형
E-Book(소장)
개인저자
Ansari, Mohammud Samar.
서명 / 저자사항
Non-linear feedback neural networks [electronic resource] : VLSI implementations and application / Mohd. Samar Ansari.
발행사항
New Delhi :   Springer India :   Imprint: Springer,   2014.  
형태사항
1 online resource (xxii, 201 p.) : ill.
총서사항
Studies in computational intelligence,1860-949X ; 508
ISBN
9788132215639
요약
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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Neural networks (Computer science). Feedback control systems. Integrated circuits --Very large scale integration.
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100 1 ▼a Ansari, Mohammud Samar.
245 1 0 ▼a Non-linear feedback neural networks ▼h [electronic resource] : ▼b VLSI implementations and application / ▼c Mohd. Samar Ansari.
260 ▼a New Delhi : ▼b Springer India : ▼b Imprint: Springer, ▼c 2014.
300 ▼a 1 online resource (xxii, 201 p.) : ▼b ill.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
490 1 ▼a Studies in computational intelligence, ▼x 1860-949X ; ▼v 508
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a 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.
520 ▼a 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Neural networks (Computer science).
650 0 ▼a Feedback control systems.
650 0 ▼a Integrated circuits ▼x Very large scale integration.
830 0 ▼a Studies in computational intelligence; ▼v 508.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-81-322-1563-9
945 ▼a KLPA
991 ▼a E-Book(소장)

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