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.
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.
Table of Contents
- Title Page
- About the Author
Chapter 1: Introduction
- 1.1 Autonomous Systems
- 1.2 The Role of Machine Learning in Autonomous Systems
- 1.3 System Identification – an Abstract Model of the Real World
- 1.4 Online versus Offline Identification
- 1.5 Adaptive and Evolving Systems
- 1.6 Evolving or Evolutionary Systems
- 1.7 Supervised versus Unsupervised Learning
- 1.8 Structure of the Book
Part I: Fundamentals
- Chapter 2: Fundamentals of Probability Theory
- Chapter 3: Fundamentals of Machine Learning and Pattern Recognition
- Chapter 4: Fundamentals of Fuzzy Systems Theory
Part II: Methodology of Autonomous Learning Systems
Chapter 5: Evolving System Structure from Streaming Data
- 5.1 Defining System Structure Based on Prior Knowledge
- 5.2 Data Space Partitioning
- 5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environments
- 5.4 Autonomous Monitoring of the Structure Quality
- 5.5 Short- and Long-Term Focal Points and Submodels
- 5.6 Simplification and Interpretability Issues
- 5.7 Conclusions
- Chapter 6: Autonomous Learning Parameters of the Local Submodels
- Chapter 7: Autonomous Predictors, Estimators, Filters, Inferential Sensors
- Chapter 8: Autonomous Learning Classifiers
- Chapter 9: Autonomous Learning Controllers
Chapter 10: Collaborative Autonomous Learning Systems
- 10.1 Distributed Intelligence Scenarios
- 10.2 Autonomous Collaborative Learning
- 10.3 Collaborative Autonomous Clustering, AutoCluster by a Team of ALSs
- 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a Team of ALSs
- 10.5 Collaborative Autonomous Classifiers AutoClassify by a Team of ALSs
- 10.6 Superposition of Local Submodels
- 10.7 Conclusions
- Chapter 5: Evolving System Structure from Streaming Data
Part III: Applications of ALS
- Chapter 11: Autonomous Learning Sensors for Chemical and Petrochemical Industries
- Chapter 12: Autonomous Learning Systems in Mobile Robotics
- Chapter 13: Autonomous Novelty Detection and Object Tracking in Video Streams
- Chapter 14: Modelling Evolving User Behaviour with ALS
- Chapter 15: Epilogue
- Title: Autonomous Learning Systems: From Data Streams to Knowledge in Real-time
- Release date: January 2013
- Publisher(s): Wiley
- ISBN: 9781118481912