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

✍ Scribed by Plamen Angelov(auth.)

• Year: 2012
• Tongue: English
• Leaves: 279
• Category: Library

✦ Synopsis

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.

Content:
Chapter 1 Introduction (pages 1–16):
Chapter 2 Fundamentals of Probability Theory (pages 17–36):
Chapter 3 Fundamentals of Machine Learning and Pattern Recognition (pages 37–59):
Chapter 4 Fundamentals of Fuzzy Systems Theory (pages 61–81):
Chapter 5 Evolving System Structure from Streaming Data (pages 83–107):
Chapter 6 Autonomous Learning Parameters of the Local Submodels (pages 109–119):
Chapter 7 Autonomous Predictors, Estimators, Filters, Inferential Sensors (pages 121–131):
Chapter 8 Autonomous Learning Classifiers (pages 133–141):
Chapter 9 Autonomous Learning Controllers (pages 143–153):
Chapter 10 Collaborative Autonomous Learning Systems (pages 155–161):
Chapter 11 Autonomous Learning Sensors for Chemical and Petrochemical Industries (pages 163–178):
Chapter 12 Autonomous Learning Systems in Mobile Robotics (pages 179–196):
Chapter 13 Autonomous Novelty Detection and Object Tracking in Video Streams (pages 197–209):
Chapter 14 Modelling Evolving User Behaviour with ALS (pages 211–222):
Chapter 15 Epilogue (pages 223–228):