Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering

2001
ISBN: 978-0-7923-7503-6
Verlag: Springer Netherlands

Buch, Englisch, Band 17, 288 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1370 g

Reihe: International Series in Intelligent Technologies

focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition. This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools and the adaptation process in which the classifiers have to be adapted, using the observations of the dynamic process. It then focuses on the problem of a changing cluster structure (new clusters, merging of clusters, splitting of clusters and the detection of gradual changes in the cluster structure). Finally, the book integrates these parts into a complete algorithm for dynamic fuzzy classifier design and classification.

Zielgruppe

Research

Autoren/Hrsg.

Angstenberger, Larisa

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein Grundlagen der Mathematik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik Mathematik Mathematik Allgemein Mathematische Logik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

1 Introduction.- 2 General Framework of Dynamic Pattern Recognition.- 3 Stages of the Dynamic Pattern Recognition Process.- 4 Dynamic Fuzzy Classifier Design with Point-Prototype Based Clustering Algorithms.- 5 Similarity Concepts for Dynamic Objects in Pattern Recognition.- 6 Applications of Dynamic Pattern Recognition Methods.- 7 Conclusions.- References.- Unsupervised Optimal Fuzzy Clustering Algorithm of Gath and Geva.- Description of Implemented Software.