Advances in Computational Intelligence

Theory and Practice
2003
ISBN: 978-3-540-43269-2
Verlag: Springer Berlin Heidelberg

Buch, Englisch, 326 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1440 g

Reihe: Natural Computing Series

The 30 coherently written chapters by leading researchers presented in this anthology are devoted to basic results achieved in computational intelligence since 1997. The book provides complete coverage of the core issues in the field, especially in fuzzy logic and control as well as for evolutionary optimization algorithms including genetic programming, in a comprehensive and systematic way. Theoretical and methodological investigations are complemented by prototypic applications for design and management tasks in electrical engineering, mechanical engineering, and chemical engineering.
This book will become a valuable source of reference for researchers active in computational intelligence. Advanced students and professionals interested in learning about and applying advanced techniques of computational intelligence will appreciate the book as a useful guide enhanced by numerous examples and applications in a variety of fields.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Schwefel, Hans-Paul

Weinert, K. D.

Wegener, Ingo

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

1 Introduction.- I. Fuzzy Logic.- 2 Mathematical Foundations of Fuzzy Inference.- 3 Data-Based Fuzzy Modeling for Complex Applications.- 4 Fuzzy Modeling to Cope with Ambiguities.- II. Evolutionary Algorithms.- 5 Theory of Evolutionary Algorithms and Genetic Programming.- 6 Design of Evolutionary Algorithms and Applications in Surface Reconstruction.- 7 Genetic Programming and Its Application in Machining Technology.- III. Machine Learning and Optimization.- 8 Novel Learning Tasks, Optimization, and Their Application.