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.

1. Introduction 10

1.1 Autonomous Systems 13

1.2 The Role of Machine Learning in Autonomous Systems 15

1.3 System Identification 20

1.3.3 Novelty detection, outliers and the link to structure innovation 20

1.4 On-line versus Off-line Identification 21

1.5 Adaptive and Evolving Systems 22

1.6 Evolving or Evolutionary Systems 23

1.7 Supervised versus Un-supervised Learning 25

1.8 Structure of the Book 26

PART I: Fundamentals 29

2. Fundamentals of Probability Theory 29

2.1 Randomness and Determinism 30

2.2 Frequentistic versus Belief-based Approach 33

2.3 Probability Densities and Moments 33

2.4 Density Estimation – Kernel-based Approach 37

2.5 Recursive Density Estimation (RDE) 40

2.6 Detecting Novelties/Anomalies/Outliers using RDE 45

2.7 Conclusion 49

3. Fundamentals of Machine Learning and Pattern Recognition 51

3.1 Pre-processing 51

3.1.1 Normalisation and standardisation 52

3.1.2 Orthogonalization of inputs/features – rPCA method 53

3.2 Clustering 56

3.2.1 Proximity measures and clusters shape 59

3.2.2 Off-line methods 60

3.2.3 Evolving clustering methods 65

3.3 Classification 73

3.3.1 Recursive LDA, rLDA 74

3.4 Conclusion 74

4. Fundamentals of Fuzzy Systems Theory 77

4.1 Fuzzy Sets 77

4.2 Fuzzy Systems, Fuzzy Rules 80

4.2.1 Fuzzy Systems of Zadeh-Mamdani Type 81

4.2.2 Takagi-Sugeno Fuzzy Systems 83

4.3 Fuzzy Systems with Non-parametric Antecedents (AnYa) 87

4.3.1 Architecture 87

4.3.2 Analysis of AnYa 90

4.4 FRB (Off-line) classifiers 91

4.5 Neuro-Fuzzy Systems 94

4.5.1 Neuro-fuzzy system architecture 94

4.5.2 Evolving NFS as a framework for autonomous learning and knowledge extraction from data streams 97

4.5.3 Linguistic interpretation of the NFS 98

4.6 State Space Perspective 99

4.7 Conclusions 100

Part II  Methodology of Autonomous Learning Systems 101

5 Evolving System Structure from Streaming Data 101

5.1 Defining system structure based on prior knowledge 101

5.2 Data Space Partitioning 102

5.2.1 Regular partitioning of the data space 103

5.2.2 Data space partitioning through clustering 104

5.2.3. Data space partitioning based on data clouds 105

5.2.4. Importance of partitioning the joint input-output data space 105

5.2.5 Principles of data space partitioning for autonomous machine learning 107

5.2.6 Dynamic data space partitioning – evolving system structure autonomously 108

5.3 Normalisation and Standardisation of Streaming Data in Evolving Environments 114

5.3.1 Standardization in an Evolving Environment 115

5.3.2 Normalisation in an Evolving Environment 116

5.4 Autonomous Monitoring of the Structure Quality 117

5.4.1 Autonomous Input Variables Selection 117

5.4.2 Autonomous Monitoring of the Age of the Local Sub-model 120

5.4.3 Autonomous Monitoring of the Utility of the Local Sub-model 122

5.4.4 Update of the Cluster Radii 123

5.5 Short- and Long-term Focal Points and Sub-models 124

5.6 Simplification and Interpretability Issues 125

5.7 Conclusion 127

6 Autonomous Learning Parameters of the Local Sub-models 129

6.1 Learning Parameters of Local Sub-models 130

6.2 Global versus Local Learning 131

6.3 Evolving Systems Structure Recursively 133

6.4 Learning Modes 137

6.5 Robustness to Outliers in Autonomous Learning 140

6.6 Conclusions 140

7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 142

7.1 Predictors, Estimators, Filters – Problem Formulation 142

7.2 Non-linear Regression 144

7.3 Time series 145

7.4 Autonomous Learning Sensors 146

7.4.1 Autonomous Sensors – Problem Definition 146

7.4.2 A brief Overview of Soft/Intelligent/Inferential Sensors 147

7.4.3 Autonomous Intelligent Sensors (AutoSense) 149

7.4.4 AutoSense Architecture 151

7.4.5 Modes of Operation of AutoSense 152

7.4.6 Autonomous Input Variable Selection 152

7.5 Conclusions 153

8. Autonomous Learning Classifiers 155

8.1 Classifying data streams 155

8.2 Why adapt the classifier structure? 155

8.3 Architecture of Autonomous Classifiers of the Family AutoClassify 157

8.3.1 AutoClassify0 159

8.3.2 AutoClassify1 159

8.4 Learning AutoClassify from Streaming Data 162

8.4.1 Learning AutoClassify0 162

8.4.2 Learning AutoClassify1 163

8.5 Analysis of AutoClassify methods 163

8.6 Conclusions 164

9. Autonomous Learning Controllers 166

9.1 Indirect Adaptive Control Scheme 167

9.2 Evolving Inverse Plant Model from On-line Streaming Data 169

9.3 Evolving Fuzzy Controller Structure from On-line Streaming Data 170

9.4 Examples of using AutoControl 172

9.5 Conclusion 177

10. Collaborative Autonomous Learning Systems 179

10.1 Distributed Intelligence Scenarios 179

10.2 Autonomous Collaborative Learning 181

10.3 Collaborative Autonomous Clustering, AutoCluster by a team of ALSs 183

10.4 Collaborative Autonomous Predictors, Estimators, Filters and AutoSense by a team of ALSs 184

10.5 Collaborative Autonomous Classifiers AutoClassify by a team of ALSs 184

10.6 Superposition of Local Sub-models 185

10.7 Conclusion 186

PART III: Applications of ALS 187

11. Autonomous Learning Sensors for Chemical and Petro-chemical Industries 187

11.1 Case Study 1: Quality of the Products in an Oil Refinery 187

11.1.1 Introduction 187

11.1.2 The current state of the art 188

11.1.3 Problem description 189

11.1.4 The data set 189

11.1.5 AutoSesnse for kerosene quality prediction 191

11.1.6 AutoSense for Abel inflammability test 193

11.2 Case Study 2: Polypropylene Manufacturing 194

11.2.1 Problem description 194

11.2.2 Drift and shift detection by cluster age derivatives 198

11.2.3 Input variables selection 200

11.3 Conclusion 201

12. Autonomous Learning Systems in Mobile Robotics 203

12.1 The mobile robot Pioneer 3DX 203

12.2 Autonomous Classifier for Landmark Recognition 205

12.2.1 Corner detection and simple mapping of an indoor environment through wall following 207

12.2.2 Outdoor landmark detection based on visual input information 210

12.2.3 VideoDiaries 214

12.2.4 Collaborative scenario 217

12.3 Autonomous Leader Follower 220

12.4 Results Analysis 223

13. Autonomous Novelty Detection and Object Tracking in Video Streams 224

13.1 Problem Definition 224

13.2 Background subtraction and KDE for detecting visual novelties 225

13.2.1 Background subtraction method 225

13.2.2 Challenges 226

13.2.3 Parametric versus non-parametric approaches 229

13.2.4 Kernel Density Estimation method 230

13.3 Detecting Visual novelties with RDE Method 231

13.4 Object Identification in Image Frames using RDE 232

13.5 Real-time Tracking in Video Streams using ALS 234

13.6 Conclusion 237

14. Modelling Evolving User Behaviour with ALS 239

14.1 User Behaviour as an evolving phenomenon 239

14.2 Designing the User Behaviour Profile 241

14.3 Applying AutoClassify0 for modelling evolving user behaviour 244

14.4 Case studies 245

14.4.1 Users of UNIX commands 245

14.4.2 Modelling activity of people in a smart home environment 247

14.4.3 Automatic scene recognition 249

14.5 Conclusions 252

15. Epilogue 254

15.1 Conclusions 254

15.2 Open Problems 258

15.3 Future Directions 259

Bibliography 261

Index 274

Glossary 291

Appendices 295

A. Mathematical Foundations 296

A1 Probability distributions 297

A2 Basic matrix properties 300

B. Pseudo-code of the basic algorithms 302

B1 Mean shift with Epanechnikov kernel 302

B2 AutoCluster 304

B3 ELM 305

B4 AutoPredict 307

B5 AutoSense 308

B6 AutoClassify0 309

B7 AutoClassify1 311

B8 AutoControl 313

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