Angelov, Plamen (2012) Autonomous Learning Systems:From Data to Knowledge in Real Time. John Willey and Sons, Chichester. ISBN 978-1-1199-5152-0
Full text not available from this repository.Abstract
Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven – there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: • Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. • Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. • Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms • Accompanied by a website hosting additional material, including the software toolbox and lecture notes Autonomous Learning Systems provides a ‘one-stop shop’ on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.
| Item Type: | Book/Report/Proceedings |
|---|---|
| Uncontrolled Keywords: | autonomous systems ; machine learning ; intelligent systems |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | Faculty of Science and Technology > School of Computing & Communications |
| ID Code: | 56239 |
| Deposited By: | ep_importer_pure |
| Deposited On: | 19 Jul 2012 17:25 |
| Refereed?: | No |
| Published?: | Published |
| Last Modified: | 03 May 2013 10:22 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/56239 |
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