Principles of Artificial Neural Networks
Daniel Graupe
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.
Categorias:
Ano:
2013
Edição:
3rd
Editora:
World Scientific Publishing Company
Idioma:
english
ISBN 10:
9814522732
ISBN 13:
9789814522731
Série:
Advanced Series in Circuits and Systems 7
Arquivo:
PDF, 4.82 MB
IPFS:
,
english, 2013
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