Buch, Englisch, 708 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1256 g
Principles, Techniques and Applications
Buch, Englisch, 708 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1256 g
ISBN: 978-3-540-20898-3
Verlag: Springer Berlin Heidelberg
Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of computational intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of fuzzy sets and logic, neural networks, evolutionary computing and belief networks. The application areas include fuzzy databases, fuzzy control, image understanding, expert systems, object recognition, criminal investigation, telecommunication networks, and intelligent robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
- Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Statik, Dynamik, Kinetik, Kinematik
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
Weitere Infos & Material
An Introduction to Computational Intelligence.- Fuzzy Sets and Relations.- Fuzzy Logic and Approximate Reasoning.- Fuzzy Logic in Process Control.- Fuzzy Pattern Recognition.- Fuzzy Databases and Possibilistic Reasoning.- to Machine Learning Using Neural Nets.- Supervised Neural Learning Algorithms.- Unsupervised Neural Learning Algorithms.- Competitive Learning Using Neural Nets.- Neuro-dynamic Programming by Reinforcement Learning.- Evolutionary Computing Algorithms.- Belief Calculus and Probabilistic Reasoning.- Reasoning in Expert Systems Using Fuzzy Petri Nets.- Fuzzy Models for Face Matching and Mood Detection.- Behavioral Synergism of Soft Computing Tools.- Object Recognition from Gray Images Using Fuzzy ADALINE Neurons.- Distributed Machine Learning Using Fuzzy Cognitive Maps.- Machine Learning Using Fuzzy Petri Nets.- Computational Intelligence in Tele-Communication Networks.- Computational Intelligence in Mobile Robotics.- Emerging Areas of Computational Intelligence.- Research Problems for Graduate Thesis and Pre-Ph D Preparatory Courses.
