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

Book Description

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Forewords
    1. Adrian Stoica
    2. Vladik Kreinovich
    3. Arthur Kordon
    4. Lawrence O. Hall
  5. Preface
  6. About the Author
  7. Chapter 1: Introduction
    1. 1.1 Autonomous Systems
    2. 1.2 The Role of Machine Learning in Autonomous Systems
    3. 1.3 System Identification – an Abstract Model of the Real World
    4. 1.4 Online versus Offline Identification
    5. 1.5 Adaptive and Evolving Systems
    6. 1.6 Evolving or Evolutionary Systems
    7. 1.7 Supervised versus Unsupervised Learning
    8. 1.8 Structure of the Book
  8. Part I: Fundamentals
    1. Chapter 2: Fundamentals of Probability Theory
      1. 2.1 Randomness and Determinism
      2. 2.2 Frequentistic versus Belief-Based Approach
      3. 2.3 Probability Densities and Moments
      4. 2.4 Density Estimation – Kernel-Based Approach
      5. 2.5 Recursive Density Estimation (RDE)
      6. 2.6 Detecting Novelties/Anomalies/Outliers using RDE
      7. 2.7 Conclusions
    2. Chapter 3: Fundamentals of Machine Learning and Pattern Recognition
      1. 3.1 Preprocessing
      2. 3.2 Clustering
      3. 3.3 Classification
      4. 3.4 Conclusions
    3. Chapter 4: Fundamentals of Fuzzy Systems Theory
      1. 4.1 Fuzzy Sets
      2. 4.2 Fuzzy Systems, Fuzzy Rules
      3. 4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa)
      4. 4.4 FRB (Offline) Classifiers
      5. 4.5 Neurofuzzy Systems
      6. 4.6 State Space Perspective
      7. 4.7 Conclusions
  9. Part II: Methodology of Autonomous Learning Systems
    1. Chapter 5: Evolving System Structure from Streaming Data
      1. 5.1 Defining System Structure Based on Prior Knowledge
      2. 5.2 Data Space Partitioning
      3. 5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environments
      4. 5.4 Autonomous Monitoring of the Structure Quality
      5. 5.5 Short- and Long-Term Focal Points and Submodels
      6. 5.6 Simplification and Interpretability Issues
      7. 5.7 Conclusions
    2. Chapter 6: Autonomous Learning Parameters of the Local Submodels
      1. 6.1 Learning Parameters of Local Submodels
      2. 6.2 Global versus Local Learning
      3. 6.3 Evolving Systems Structure Recursively
      4. 6.4 Learning Modes
      5. 6.5 Robustness to Outliers in Autonomous Learning
      6. 6.6 Conclusions
    3. Chapter 7: Autonomous Predictors, Estimators, Filters, Inferential Sensors
      1. 7.1 Predictors, Estimators, Filters – Problem Formulation
      2. 7.2 Nonlinear Regression
      3. 7.3 Time Series
      4. 7.4 Autonomous Learning Sensors
      5. 7.5 Conclusions
    4. Chapter 8: Autonomous Learning Classifiers
      1. 8.1 Classifying Data Streams
      2. 8.2 Why Adapt the Classifier Structure?
      3. 8.3 Architecture of Autonomous Classifiers of the Family AutoClassify
      4. 8.4 Learning AutoClassify from Streaming Data
      5. 8.5 Analysis of AutoClassify
      6. 8.6 Conclusions
    5. Chapter 9: Autonomous Learning Controllers
      1. 9.1 Indirect Adaptive Control Scheme
      2. 9.2 Evolving Inverse Plant Model from Online Streaming Data
      3. 9.3 Evolving Fuzzy Controller Structure from Online Streaming Data
      4. 9.4 Examples of Using AutoControl
      5. 9.5 Conclusions
    6. Chapter 10: Collaborative Autonomous Learning Systems
      1. 10.1 Distributed Intelligence Scenarios
      2. 10.2 Autonomous Collaborative Learning
      3. 10.3 Collaborative Autonomous Clustering, AutoCluster by a Team of ALSs
      4. 10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a Team of ALSs
      5. 10.5 Collaborative Autonomous Classifiers AutoClassify by a Team of ALSs
      6. 10.6 Superposition of Local Submodels
      7. 10.7 Conclusions
  10. Part III: Applications of ALS
    1. Chapter 11: Autonomous Learning Sensors for Chemical and Petrochemical Industries
      1. 11.1 Case Study 1: Quality of the Products in an Oil Refinery
      2. 11.2 Case Study 2: Polypropylene Manufacturing
      3. 11.3 Conclusions
    2. Chapter 12: Autonomous Learning Systems in Mobile Robotics
      1. 12.1 The Mobile Robot Pioneer 3DX
      2. 12.2 Autonomous Classifier for Landmark Recognition
      3. 12.3 Autonomous Leader Follower
      4. 12.4 Results Analysis
    3. Chapter 13: Autonomous Novelty Detection and Object Tracking in Video Streams
      1. 13.1 Problem Definition
      2. 13.2 Background Subtraction and KDE for Detecting Visual Novelties
      3. 13.3 Detecting Visual Novelties with the RDE Method
      4. 13.4 Object Identification in Image Frames Using RDE
      5. 13.5 Real-Time Tracking in Video Streams Using ALS
      6. 13.6 Conclusions
    4. Chapter 14: Modelling Evolving User Behaviour with ALS
      1. 14.1 User Behaviour as an Evolving Phenomenon
      2. 14.2 Designing the User Behaviour Profile
      3. 14.3 Applying AutoClassify0 for Modelling Evolving User Behaviour
      4. 14.4 Case Studies
      5. 14.5 Conclusions
    5. Chapter 15: Epilogue
      1. 15.1 Conclusions
      2. 15.2 Open Problems
      3. 15.3 Future Directions
    6. Appendices
      1. Appendix A: Mathematical Foundations
        1. A.1 Probability Distributions
        2. A.2 Basic Matrix Properties
      2. Appendix B: Pseudocode of the Basic Algorithms
        1. B.1 Mean Shift with Epanechnikov Kernel
        2. B.2 AutoCluster
        3. B.3 ELM
        4. B.4 AutoCluster
        5. B.5 AutoSense
        6. B.6 AutoClassify0
        7. B.7 AutoClassify1
        8. B.8 AutoControl
  11. References
  12. Glossary
  13. Index