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First dedicated book on non-linear feedback neural networks
Contains thorough discussion on transcendental energy function
Includes special chapter on Hopfield Network, its applications, and limitations
Cadence OrCAD circuit files for all the circuit simulations discussed in the book
Useful material for researchers working in the area of analog computation
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.
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.