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.
A propos de l’auteur
Plamen Parvanov Angelov, Lancaster University, UK
Plamen Parvanov is a senior lecturer in the School of Computing and Communications at Lancaster University. He is an Associate Editor of three international journals and the founding co-Editor-in-Chief of the Springer journal Evolving Systems. He is also the Vice Chair of the Technical Committee on Standards, Computational Intelligence Society, IEEE and co-Chair of several IEEE conferences. His research in UAV/UAS is often publicised in external publications, e.g. the prestigious Computational Intelligence Magazine; Aviation Week, Flight Global, Airframer, Flight International, etc. His research focuses on computational intelligence and evolving systems, and his research in to autonomous systems has received worldwide recognition. As the Principle Investigator at Lancaster University for a team working on UAV Sense and Avoid fortwo projects of ASTRAEA his work was recognised by ‘The Engineer Innovation and Technology 2008 Award in two categories: i) Aerospace and Defence and ii) The Special Award which is an outstanding achievement.
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