Radial Basis Function Networks 1

Recent Developments in Theory and Applications
2001
ISBN: 978-3-7908-1367-8
Verlag: Physica-Verlag HD

Buch, Englisch, 318 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1440 g

Reihe: Studies in Fuzziness and Soft Computing

The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.

Zielgruppe

Research

Autoren/Hrsg.

C. Jain, Lakhmi

J.Howlett, Robert

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Dynamic RBF networks.- A hyperrectangle-based method that creates RBF networks.- Hierarchical radial basis function networks.- RBF neural networks with orthogonal basis functions.- On noise-immune RBF networks.- Robust RBF networks.- An introduction to kernel methods.- Unsupervised learning using radial kernels.- RBF learning in a non-stationary environment: the stability-plasticity dilemma.- A new learning theory and polynomial-time autonomous learning algorithms for generating RBF networks.- Evolutionary optimization of RBF networks.