Agent-Based Hybrid Intelligent Systems

Chengqi Zhang

ISBN 10: 3540209085 / ISBN 13: 9783540209089

Verlag: Springer Berlin Heidelberg Jan 2004, 2004

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems.This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining. 216 pp. Englisch.

Bestandsnummer des Verkäufers 9783540209089

Bibliografische Details

• Titel: Agent-Based Hybrid Intelligent Systems
• Verlag: Springer Berlin Heidelberg Jan 2004
• Erscheinungsjahr: 2004
• Sprache: Englisch
• ISBN-10: 3540209085
• ISBN-13: 9783540209089
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 12 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 12 mm
• Gewicht: 335 Gramm

Über diesen Titel

Inhaltsangabe

Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems.

This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining.