An Introduction to Neural Networks

1. Auflage 1997
ISBN: 978-1-85728-673-1
Verlag: Taylor & Francis

Buch, Englisch, 248 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 498 g

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Zielgruppe

Undergraduate

Autoren/Hrsg.

Gurney, Kevin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Betriebssysteme

Inhaltsverzeichnis

Neural Net - A Preliminary Discussion. The von Neumann Machine and The Symbolic Paradigm. Real Neurons - A Review. Artificial neurons. Non- binary signal communication. Introducing Time. Network Features. Alternative Node Types. Cubic Nodes and Reward. Penalty Training. Drawing Things Together - Some Perspectives.