Evolving Connectionist Systems

The Knowledge Engineering Approach
2. Auflage 2007
ISBN: 978-1-84628-345-1
Verlag: Springer

Buch, Englisch, 451 Seiten, Format (B × H): 154 mm x 235 mm, Gewicht: 668 g

This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.

Zielgruppe

Research

Autoren/Hrsg.

Kasabov, Nikola K

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Biowissenschaften
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering Objektorientierte Softwareentwicklung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung

Inhaltsverzeichnis

Evolving Connectionist Methods.- Feature Selection, Model Creation, and Model Validation.- Evolving Connectionist Methods for Unsupervised Learning.- Evolving Connectionist Methods for Supervised Learning.- Brain Inspired Evolving Connectionist Models.- Evolving Neuro-Fuzzy Inference Models.- Population-Generation-Based Methods: Evolutionary Computation.- Evolving Integrated Multimodel Systems.- Evolving Intelligent Systems.- Adaptive Modelling and Knowledge Discovery in Bioinformatics.- Dynamic Modelling of Brain Functions and Cognitive Processes.- Modelling the Emergence of Acoustic Segments in Spoken Languages.- Evolving Intelligent Systems for Adaptive Speech Recognition.- Evolving Intelligent Systems for Adaptive Image Processing.- Evolving Intelligent Systems for Adaptive Multimodal Information Processing.- Evolving Intelligent Systems for Robotics and Decision Support.- What Is Next: Quantum Inspired Evolving Intelligent Systems?.

Über Autor(innen)

Professor Nik Kasabov is the Founding Director and Chief Scientist of the Knowledge Engineering and Discovery Research Institute, Auckland, NZ. He holds a number of key positions, including Chair of the Adaptive Systems Task Force of the Neural Network Technical Committee of the IEEE. He has published extensively, and been Programme Chair of over 50 high-profile conferences.