Non-Linear Feedback Neural Networks [electronic resource] : VLSI Implementations and Applications / by Mohd. Samar Ansari.

By: Ansari, Mohd. Samar [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Studies in Computational Intelligence: 508Publisher: New Delhi : Springer India : Imprint: Springer, 2014Description: XXII, 201 p. 79 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9788132215639Subject(s): Engineering | Electronics | Systems engineering | Engineering | Computational Intelligence | Circuits and Systems | Mathematical Models of Cognitive Processes and Neural Networks | Electronics and Microelectronics, InstrumentationAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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.

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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