Principles Of Artificial Neural Networks (3rd Edition) (Hardcover, 3rd Revised edition)

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

General

Imprint:	World Scientific Publishing Co Pte Ltd
Country of origin:	Singapore
Series:	Advanced Series In Circuits And Systems, 7
Release date:	September 2013
First published:	July 2013
Authors:	Daniel Graupe
Dimensions:	249 x 173 x 23mm (L x W x T)
Format:	Hardcover
Pages:	384
Edition:	3rd Revised edition
ISBN-13:	978-981-4522-73-1
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	981-4522-73-2
Barcode:	9789814522731

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