Principles Of Artificial Neural Networks (3rd 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.

Product details

• Authors: Daniel Graupe, Daniel Graupe, Graupe Daniel
• Publisher: World Scientific Publishing
• Content: Book
• Product form: Hardback
• Publication date: 18.09.2013
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT
• EAN: 9789814522731
• ISBN: 978-981-45227-3-1
• Pages: 384
• Illustrations: Illustrations (black and white)
• Series: Advanced Circuits and Systems > 7; Advanced Series In Circuits And Systems > 7
• Subjects: COMPUTERS / Data Science / Neural Networks; Neural networks and fuzzy systems; Neural networks & fuzzy systems