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Evolving Connectionist Systems: The Knowledge Engineering Approach

(Second, Extended Edition)
By Nikola Kasabov, Auckland University of Technology, Auckland, New Zealand

Evolving Connectionist Systems book cover

Evolving Connectionist Systems: The Knowledge Engineering Approach
(Second, Extended Edition)

Kasabov, Nikola
Originally published in the Series: Perspectives in Neural Computing
2nd ed., 2007, XXII, 458 p., 185 illus., Softcover
ISBN: 978-1-84628-345-1

About this book
This second edition of Evolving Connectionist Systems presents generic computational methods and techniques for evolvable, adaptive, knowledge-based models and systems. This edition includes new methods, such as: adaptive model and data integration, adaptive model and feature optimisation, neuro-fuzzy personalised modelling, computational neuro-genetic modelling, quantum inspired information processing, along with new applications in: bioinformatics, brain data modelling, adaptive robots, adaptive decision support systems, adaptive multimodal signal processing. The models and techniques presented are connectionist-based, integrating neural networks, fuzzy rule-based systems, evolutionary computation, and statistical techniques, featuring adaptive learning and knowledge discovery. Divided into two parts, the book opens with evolving processes in nature; looks at methods and techniques for adaptive, evolving, knowledge-based learning; then covers bioinformatics and brain data modelling and knowledge discovery; finishing with various applications for intelligent systems. The book is aimed at all those interested in developing adaptive models and systems to solve challenging real world problems in information sciences and engineering.

Table of contents
Part 1 Evolving Connectionist Methods – Modelling and Knowledge Discovery from Dynamic, Evolving Information Processes – Feature selection, Model Creation and Model Validation – Evolving Connectionist Methods for Unsupervised Learning – Evolving Connectionist Methods for Supervised Learning – Evolving State-Based and Spiking Neural Networks – Evolving Neuro-Fuzzy Inference Methods – Population/Generation-Based Methods: Evolutionary Computation - Evolving Integrated Multi-Model Systems – Part II Evolving Intelligent Systems – Adaptive Modelling and Knowledge Discovery in Bioinformatics – Adaptive Modelling and Knowledge Discovery from Brain Data – Modelling the Emergence of Acoustic Segments in Spoken Languages – Adaptive Speech Recognition – Adaptive Image Processing – Adaptive Multi-Modal Signal Processing – Evolving Robotics and Socio-Economic Systems - Quantum Inspired Evolving Intelligent Systems.

Color figures from this book
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http://www.springer.com/computer/ai/book/978-1-84628-345-1


Last updated: 02 Dec 2011 10:07am

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