TY  - GEN
AB  - 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.
AU  - Ansari, Mohd. Samar,
CN  - SpringerLink
CN  - QA76.87
DO  - 10.1007/978-81-322-1563-9
DO  - doi
ID  - 696752
KW  - Neural networks (Computer science)
KW  - Feedback control systems.
KW  - Integrated circuits
LK  - https://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-81-322-1563-9
N2  - 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.
SN  - 9788132215639 
SN  - 813221563X 
T1  - Non-linear feedback neural networksVLSI implementations and applications /
TI  - Non-linear feedback neural networksVLSI implementations and applications /
UR  - https://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-81-322-1563-9
VL  - v.508
ER  -