Details

Autonomous Learning Systems


Autonomous Learning Systems

From Data Streams to Knowledge in Real-time
1. Aufl.

von: Plamen Angelov

114,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 15.10.2012
ISBN/EAN: 9781118481905
Sprache: englisch
Anzahl Seiten: 304

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Beschreibungen

<p><i>Autonomous Learning Systems</i> 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.</p> <p>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. </p> <p>Key features: </p> <ul> <li>Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications.</li> <li>Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition.</li> <li>Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms.</li> <li>Accompanied by a website hosting additional material, including the software toolbox and lecture notes.</li> </ul> <p><i>Autonomous Learning Systems</i> 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.</p>
<p>Forewords xi</p> <p>Preface xix</p> <p>About the Author xxiii</p> <p><b>1 Introduction 1</b></p> <p>1.1 Autonomous Systems 3</p> <p>1.2 The Role of Machine Learning in Autonomous Systems 4</p> <p>1.3 System Identification – an Abstract Model of the Real World 6</p> <p>1.4 Online versus Offline Identification 9</p> <p>1.5 Adaptive and Evolving Systems 10</p> <p>1.6 Evolving or Evolutionary Systems 11</p> <p>1.7 Supervised versus Unsupervised Learning 13</p> <p>1.8 Structure of the Book 14</p> <p><b>PART I FUNDAMENTALS</b></p> <p><b>2 Fundamentals of Probability Theory 19</b></p> <p>2.1 Randomness and Determinism 20</p> <p>2.2 Frequentistic versus Belief-Based Approach 22</p> <p>2.3 Probability Densities and Moments 23</p> <p>2.4 Density Estimation – Kernel-Based Approach 26</p> <p>2.5 Recursive Density Estimation (RDE) 28</p> <p>2.6 Detecting Novelties/Anomalies/Outliers using RDE 32</p> <p>2.7 Conclusions 36</p> <p><b>3 Fundamentals of Machine Learning and Pattern Recognition 37</b></p> <p>3.1 Preprocessing 37</p> <p>3.2 Clustering 42</p> <p>3.3 Classification 56</p> <p>3.4 Conclusions 58</p> <p><b>4 Fundamentals of Fuzzy Systems Theory 61</b></p> <p>4.1 Fuzzy Sets 61</p> <p>4.2 Fuzzy Systems, Fuzzy Rules 64</p> <p>4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69</p> <p>4.4 FRB (Offline) Classifiers 73</p> <p>4.5 Neurofuzzy Systems 75</p> <p>4.6 State Space Perspective 79</p> <p>4.7 Conclusions 81</p> <p><b>PART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS</b></p> <p><b>5 Evolving System Structure from Streaming Data 85</b></p> <p>5.1 Defining System Structure Based on Prior Knowledge 85</p> <p>5.2 Data Space Partitioning 86</p> <p>5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environment 96</p> <p>5.4 Autonomous Monitoring of the Structure Quality 98</p> <p>5.5 Short- and Long-Term Focal Points and Submodels 104</p> <p>5.6 Simplification and Interpretability Issues 105</p> <p>5.7 Conclusions 107</p> <p><b>6 Autonomous Learning Parameters of the Local Submodels 109</b></p> <p>6.1 Learning Parameters of Local Submodels 110</p> <p>6.2 Global versus Local Learning 111</p> <p>6.3 Evolving Systems Structure Recursively 113</p> <p>6.4 Learning Modes 116</p> <p>6.5 Robustness to Outliers in Autonomous Learning 118</p> <p>6.6 Conclusions 118</p> <p><b>7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 121</b></p> <p>7.1 Predictors, Estimators, Filters – Problem Formulation 121</p> <p>7.2 Nonlinear Regression 123</p> <p>7.3 Time Series 124</p> <p>7.4 Autonomous Learning Sensors 125</p> <p>7.5 Conclusions 131</p> <p><b>8 Autonomous Learning Classifiers 133</b></p> <p>8.1 Classifying Data Streams 133</p> <p>8.2 Why Adapt the Classifier Structure? 134</p> <p>8.3 Architecture of Autonomous Classifiers of the Family Auto<i>Classify</i> 135</p> <p>8.4 Learning <i>AutoClassify</i> from Streaming Data 139</p> <p>8.5 Analysis of <i>AutoClassify</i> 140</p> <p>8.6 Conclusions 140</p> <p><b>9 Autonomous Learning Controllers 143</b></p> <p>9.1 Indirect Adaptive Control Scheme 144</p> <p>9.2 Evolving Inverse Plant Model from Online Streaming Data 145</p> <p>9.3 Evolving Fuzzy Controller Structure from Online Streaming Data 147</p> <p>9.4 Examples of Using <i>AutoControl</i> 148</p> <p>9.5 Conclusions 153</p> <p><b>10 Collaborative Autonomous Learning Systems 155</b></p> <p>10.1 Distributed Intelligence Scenarios 155</p> <p>10.2 Autonomous Collaborative Learning 157</p> <p>10.3 Collaborative Autonomous Clustering, <i>AutoCluster</i> by a Team of ALSs 158</p> <p>10.4 Collaborative Autonomous Predictors, Estimators, Filters and <i>AutoSense</i> by a Team of ALSs 159</p> <p>10.5 Collaborative Autonomous Classifiers <i>AutoClassify</i> by a Team of ALSs 160</p> <p>10.6 Superposition of Local Submodels 161</p> <p>10.7 Conclusions 161</p> <p><b>PART III APPLICATIONS OF ALS</b></p> <p><b>11 Autonomous Learning Sensors for Chemical and Petrochemical Industries 165</b></p> <p>11.1 Case Study 1: Quality of the Products in an Oil Refinery 165</p> <p>11.2 Case Study 2: Polypropylene Manufacturing 172</p> <p>11.3 Conclusions 178</p> <p><b>12 Autonomous Learning Systems in Mobile Robotics 179</b></p> <p>12.1 The Mobile Robot Pioneer 3DX 179</p> <p>12.2 Autonomous Classifier for Landmark Recognition 180</p> <p>12.3 Autonomous Leader Follower 193</p> <p>12.4 Results Analysis 196</p> <p><b>13 Autonomous Novelty Detection and Object Tracking in Video Streams 197</b></p> <p>13.1 Problem Definition 197</p> <p>13.2 Background Subtraction and KDE for Detecting Visual Novelties 198</p> <p>13.3 Detecting Visual Novelties with the RDE Method 203</p> <p>13.4 Object Identification in Image Frames Using RDE 204</p> <p>13.5 Real-time Tracking in Video Streams Using ALS 206</p> <p>13.6 Conclusions 209</p> <p><b>14 Modelling Evolving User Behaviour with ALS 211</b></p> <p>14.1 User Behaviour as an Evolving Phenomenon 211</p> <p>14.2 Designing the User Behaviour Profile 212</p> <p>14.3 Applying <i>AutoClassify0</i> for Modelling Evolving User Behaviour 215</p> <p>14.4 Case Studies 216</p> <p>14.5 Conclusions 221</p> <p><b>15 Epilogue 223</b></p> <p>15.1 Conclusions 223</p> <p>15.2 Open Problems 227</p> <p>15.3 Future Directions 227</p> <p><b>APPENDICES</b></p> <p>Appendix A Mathematical Foundations 231</p> <p>Appendix B Pseudocode of the Basic Algorithms 235</p> <p>References 245</p> <p>Glossary 259</p> <p>Index 263</p>
<p>“Overall, this book presents a valuable framework for further investigation and development for researchers and software developers. Summing Up: Recommended. Graduate students and above.”  (<i>Choice</i>, 1 October 2013)</p>
<p><strong>Plamen Parvanov Angelov, Lancaster University, UK</strong><br />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 <em>Evolving Systems</em>. 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 <em>Computational Intelligence Magazine</em>; <em>Aviation Week</em>, <em>Flight Global</em>, <em>Airframer</em>, <em>Flight International</em>, 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.
<p><i>Autonomous Learning Systems</i> 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.</p> <p>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. </p> <p>Key features: </p> <ul> <li>Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications.</li> <li>Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition.</li> <li>Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms.</li> <li>Accompanied by a website hosting additional material, including the software toolbox and lecture notes.</li> </ul> <p>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.</p>

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