Nikola Kasabov

Evolving Connectionist Systems

The Knowledge Engineering Approach

• Broschiertes Buch

Produktbeschreibung

This second edition of Evolving Connectionist Systems presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems, as well as new trends including computational neuro-genetic modelling 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. The models and techniques used are connectionist-based and, where possible, existing connectionist models have been used and extended. Divided into four parts the book opens with evolving processes in nature; looks at methods and techniques that can be used in evolving connectionist systems; then covers various applications in bioinformatics and brain studies; finishing with applications for intelligent machines. Aimed at all those interested in developing adaptive models and systems to solve challenging real world problems in computer science and engineering.

Produktdetails

• Verlag: Springer, Berlin
• Artikelnr. des Verlages: 11590705
• 2nd ed.
• Seitenzahl: 451
• Erscheinungstermin: 15. August 2007
• Englisch
• Abmessung: 235mm x 154mm x 24mm
• Gewicht: 668g
• ISBN-13: 9781846283451
• ISBN-10: 1846283450
• Artikelnr.: 22706391

Autorenporträt

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.

Inhaltsangabe

From the contents
Part 1 Evolving Connectionist Systems: Methods and Techniques Introduction: Evolving Information Processes and Evolving Intelligence Feature selection, Model Creation and Model Validation: Statistical Learning Approaches Unsupervised Learning: Clustering and Vector Quantisation Supervised Learning in Connectionist Systems Recurrent Neural Networks. Finite Automata. Spiking Neural Networks Neuro-Fuzzy Inference Systems Evolutionary Computation for Model and Feature Optimisation - Evolving Integrated Multi-modal Systems Part II Inspiration from-, and Applications to Natural Biological Systems Data Analysis, Modelling and Knowledge Discovery in Bioinformatics Dynamic Modelling of Brain Functions and Cognitive Processes Modelling the Emergence of Acoustic Segments from Spoken Languages Part III Evolving Intelligent Systems Adaptive Speech Recognition Adaptive Image Processing Adaptive Multi-modal Systems Evolving Robotics and Socio-Economic Systems.