Main Principles of Artificial Neural Networks

Principles of Artificial Neural Networks

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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.

Year:
2013
Edition:
3rd
Publisher:
World Scientific Publishing Company
Language:
english
Pages:
383
ISBN 10:
9814522732
ISBN 13:
9789814522731
Series:
Advanced Series in Circuits and Systems 7
File:
PDF, 4.82 MB
Download (pdf, 4.82 MB)

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