Autonomous Learning Systems: From Data Streams to Knowledge by Plamen Angelov

By Plamen Angelov

Autonomous studying Systems is the results of over a decade of centred study and stories during this rising region which spans a few famous and well-established disciplines that come with computing device studying, approach identity, info mining, fuzzy common sense, neural networks, neuro-fuzzy structures, keep an eye on conception and development acceptance. The evolution of those structures has been either industry-driven with an expanding call for from sectors resembling defence and protection, aerospace and complex approach industries, bio-medicine and clever transportation, in addition to research-driven – there's a powerful development of innovation of all the above well-established study disciplines that's associated with their online and real-time software; their adaptability and flexibility.

Providing an creation to the major applied sciences, exact technical factors of the method, and an indication of the sensible relevance of the procedure with a variety of functions, this ebook addresses the demanding situations of independent studying platforms with a scientific process that lays the principles for a quick growing to be zone of analysis that would underpin a variety of technological functions very important to either and society. 

Key features: 

  • Presents the topic systematically from explaining the basics to illustrating the proposed procedure with quite a few applications.
  • Covers a variety of functions in fields together with unmanned vehicles/robotics, oil refineries, chemical undefined, evolving person behaviour and task recognition.
  • Reviews conventional fields together with clustering, type, keep an eye on, fault detection and anomaly detection, filtering and estimation during the prism of evolving and autonomously studying mechanisms.
  • Accompanied via an internet site website hosting extra fabric, together with the software program toolbox and lecture notes.

Autonomous studying Systems presents a ‘one-stop store’ at the topic for lecturers, scholars, researchers and working towards engineers. it's also a worthy reference for presidency enterprises and software program developers.

Content:
Chapter 1 creation (pages 1–16):
Chapter 2 basics of chance idea (pages 17–36):
Chapter three basics of computing device studying and development attractiveness (pages 37–59):
Chapter four basics of Fuzzy platforms idea (pages 61–81):
Chapter five Evolving approach constitution from Streaming facts (pages 83–107):
Chapter 6 self sufficient studying Parameters of the neighborhood Submodels (pages 109–119):
Chapter 7 independent Predictors, Estimators, Filters, Inferential Sensors (pages 121–131):
Chapter eight independent studying Classifiers (pages 133–141):
Chapter nine independent studying Controllers (pages 143–153):
Chapter 10 Collaborative self sufficient studying structures (pages 155–161):
Chapter eleven self sufficient studying Sensors for Chemical and Petrochemical Industries (pages 163–178):
Chapter 12 independent studying platforms in cellular Robotics (pages 179–196):
Chapter thirteen self sustaining Novelty Detection and item monitoring in Video Streams (pages 197–209):
Chapter 14 Modelling Evolving consumer Behaviour with ALS (pages 211–222):
Chapter 15 Epilogue (pages 223–228):

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Extra info for Autonomous Learning Systems: From Data Streams to Knowledge in Real-time

Example text

It is closely related to (albeit developing independently from) the works on self-organising systems (Lin, Lin and Shen, 2001; Juang and Lin, 1999) and growing neural networks (Fritzke, 1994). In the late 1990s and until 2001–2002 the term ‘evolving’ was also used in a different context – in terms of evolutionary (this will be clarified in the next section). Since 2002 and especially since 2006 when the IEEE started supporting regular annual conferences and other events (the last one, the 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems, being in May 2012 in Madrid) it is used for dynamically evolving in terms of system structure systems.

23) – is not. 28) The proposed expression for the density is nonparametric (it does not have even the pretty generic parameter, h). It does depend on the relative data distribution while the standard probability density distribution assumes independence of the observations, which is true only for handful of problems like tossing coins, throwing die, lottery and other ‘purely’ random phenomena. 1)) usually takes into account only two consecutive observations and cannot take into account all past measurements (this is the reason popular hidden Markov models, HMM are usually considered of so-called first order only).

It is true, however, that evolving systems are also adaptive, but the subject of the adaptation are both system parameters as with the adaptive (in a narrow sense) systems as well as its structure. 6. The area of evolving systems (as described above) that was conceived around the turn of the century (Angelov and Buswell, 2001; Angelov, 2002; Kasabov and Song, 2002) is still under intensive development and ‘fermentation’. It is closely related to (albeit developing independently from) the works on self-organising systems (Lin, Lin and Shen, 2001; Juang and Lin, 1999) and growing neural networks (Fritzke, 1994).

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