{"product_id":"autonomous-learning-systems-isbn-9781119951520","title":"Autonomous Learning Systems","description":"\u003cp\u003e\u003ci\u003eAutonomous Learning Systems\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eProviding 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. \u003c\/p\u003e \u003cp\u003eKey features: \u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications.\u003c\/li\u003e \u003cli\u003eCovers a wide range of applications in fields including unmanned vehicles\/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition.\u003c\/li\u003e \u003cli\u003eReviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms.\u003c\/li\u003e \u003cli\u003eAccompanied by a website hosting additional material, including the software toolbox and lecture notes.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAutonomous Learning Systems\u003c\/i\u003e 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.\u003c\/p\u003e  \u003cp\u003eForewords xi\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAbout the Author xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Autonomous Systems 3\u003c\/p\u003e \u003cp\u003e1.2 The Role of Machine Learning in Autonomous Systems 4\u003c\/p\u003e \u003cp\u003e1.3 System Identification – an Abstract Model of the Real World 6\u003c\/p\u003e \u003cp\u003e1.4 Online versus Offline Identification 9\u003c\/p\u003e \u003cp\u003e1.5 Adaptive and Evolving Systems 10\u003c\/p\u003e \u003cp\u003e1.6 Evolving or Evolutionary Systems 11\u003c\/p\u003e \u003cp\u003e1.7 Supervised versus Unsupervised Learning 13\u003c\/p\u003e \u003cp\u003e1.8 Structure of the Book 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I FUNDAMENTALS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Fundamentals of Probability Theory 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Randomness and Determinism 20\u003c\/p\u003e \u003cp\u003e2.2 Frequentistic versus Belief-Based Approach 22\u003c\/p\u003e \u003cp\u003e2.3 Probability Densities and Moments 23\u003c\/p\u003e \u003cp\u003e2.4 Density Estimation – Kernel-Based Approach 26\u003c\/p\u003e \u003cp\u003e2.5 Recursive Density Estimation (RDE) 28\u003c\/p\u003e \u003cp\u003e2.6 Detecting Novelties\/Anomalies\/Outliers using RDE 32\u003c\/p\u003e \u003cp\u003e2.7 Conclusions 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fundamentals of Machine Learning and Pattern Recognition 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Preprocessing 37\u003c\/p\u003e \u003cp\u003e3.2 Clustering 42\u003c\/p\u003e \u003cp\u003e3.3 Classification 56\u003c\/p\u003e \u003cp\u003e3.4 Conclusions 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Fundamentals of Fuzzy Systems Theory 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Fuzzy Sets 61\u003c\/p\u003e \u003cp\u003e4.2 Fuzzy Systems, Fuzzy Rules 64\u003c\/p\u003e \u003cp\u003e4.3 Fuzzy Systems with Nonparametric Antecedents (AnYa) 69\u003c\/p\u003e \u003cp\u003e4.4 FRB (Offline) Classifiers 73\u003c\/p\u003e \u003cp\u003e4.5 Neurofuzzy Systems 75\u003c\/p\u003e \u003cp\u003e4.6 State Space Perspective 79\u003c\/p\u003e \u003cp\u003e4.7 Conclusions 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II METHODOLOGY OF AUTONOMOUS LEARNING SYSTEMS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Evolving System Structure from Streaming Data 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Defining System Structure Based on Prior Knowledge 85\u003c\/p\u003e \u003cp\u003e5.2 Data Space Partitioning 86\u003c\/p\u003e \u003cp\u003e5.3 Normalisation and Standardisation of Streaming Data in an Evolving Environment 96\u003c\/p\u003e \u003cp\u003e5.4 Autonomous Monitoring of the Structure Quality 98\u003c\/p\u003e \u003cp\u003e5.5 Short- and Long-Term Focal Points and Submodels 104\u003c\/p\u003e \u003cp\u003e5.6 Simplification and Interpretability Issues 105\u003c\/p\u003e \u003cp\u003e5.7 Conclusions 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Autonomous Learning Parameters of the Local Submodels 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Learning Parameters of Local Submodels 110\u003c\/p\u003e \u003cp\u003e6.2 Global versus Local Learning 111\u003c\/p\u003e \u003cp\u003e6.3 Evolving Systems Structure Recursively 113\u003c\/p\u003e \u003cp\u003e6.4 Learning Modes 116\u003c\/p\u003e \u003cp\u003e6.5 Robustness to Outliers in Autonomous Learning 118\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Autonomous Predictors, Estimators, Filters, Inferential Sensors 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Predictors, Estimators, Filters – Problem Formulation 121\u003c\/p\u003e \u003cp\u003e7.2 Nonlinear Regression 123\u003c\/p\u003e \u003cp\u003e7.3 Time Series 124\u003c\/p\u003e \u003cp\u003e7.4 Autonomous Learning Sensors 125\u003c\/p\u003e \u003cp\u003e7.5 Conclusions 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Autonomous Learning Classifiers 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Classifying Data Streams 133\u003c\/p\u003e \u003cp\u003e8.2 Why Adapt the Classifier Structure? 134\u003c\/p\u003e \u003cp\u003e8.3 Architecture of Autonomous Classifiers of the Family Auto\u003ci\u003eClassify\u003c\/i\u003e 135\u003c\/p\u003e \u003cp\u003e8.4 Learning \u003ci\u003eAutoClassify\u003c\/i\u003e from Streaming Data 139\u003c\/p\u003e \u003cp\u003e8.5 Analysis of \u003ci\u003eAutoClassify\u003c\/i\u003e 140\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Autonomous Learning Controllers 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Indirect Adaptive Control Scheme 144\u003c\/p\u003e \u003cp\u003e9.2 Evolving Inverse Plant Model from Online Streaming Data 145\u003c\/p\u003e \u003cp\u003e9.3 Evolving Fuzzy Controller Structure from Online Streaming Data 147\u003c\/p\u003e \u003cp\u003e9.4 Examples of Using \u003ci\u003eAutoControl\u003c\/i\u003e 148\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Collaborative Autonomous Learning Systems 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Distributed Intelligence Scenarios 155\u003c\/p\u003e \u003cp\u003e10.2 Autonomous Collaborative Learning 157\u003c\/p\u003e \u003cp\u003e10.3 Collaborative Autonomous Clustering, \u003ci\u003eAutoCluster\u003c\/i\u003e by a Team of ALSs 158\u003c\/p\u003e \u003cp\u003e10.4 Collaborative Autonomous Predictors, Estimators, Filters and \u003ci\u003eAutoSense\u003c\/i\u003e by a Team of ALSs 159\u003c\/p\u003e \u003cp\u003e10.5 Collaborative Autonomous Classifiers \u003ci\u003eAutoClassify\u003c\/i\u003e by a Team of ALSs 160\u003c\/p\u003e \u003cp\u003e10.6 Superposition of Local Submodels 161\u003c\/p\u003e \u003cp\u003e10.7 Conclusions 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III APPLICATIONS OF ALS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Autonomous Learning Sensors for Chemical and Petrochemical Industries 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Case Study 1: Quality of the Products in an Oil Refinery 165\u003c\/p\u003e \u003cp\u003e11.2 Case Study 2: Polypropylene Manufacturing 172\u003c\/p\u003e \u003cp\u003e11.3 Conclusions 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Autonomous Learning Systems in Mobile Robotics 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 The Mobile Robot Pioneer 3DX 179\u003c\/p\u003e \u003cp\u003e12.2 Autonomous Classifier for Landmark Recognition 180\u003c\/p\u003e \u003cp\u003e12.3 Autonomous Leader Follower 193\u003c\/p\u003e \u003cp\u003e12.4 Results Analysis 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Autonomous Novelty Detection and Object Tracking in Video Streams 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Problem Definition 197\u003c\/p\u003e \u003cp\u003e13.2 Background Subtraction and KDE for Detecting Visual Novelties 198\u003c\/p\u003e \u003cp\u003e13.3 Detecting Visual Novelties with the RDE Method 203\u003c\/p\u003e \u003cp\u003e13.4 Object Identification in Image Frames Using RDE 204\u003c\/p\u003e \u003cp\u003e13.5 Real-time Tracking in Video Streams Using ALS 206\u003c\/p\u003e \u003cp\u003e13.6 Conclusions 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Modelling Evolving User Behaviour with ALS 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 User Behaviour as an Evolving Phenomenon 211\u003c\/p\u003e \u003cp\u003e14.2 Designing the User Behaviour Profile 212\u003c\/p\u003e \u003cp\u003e14.3 Applying \u003ci\u003eAutoClassify0\u003c\/i\u003e for Modelling Evolving User Behaviour 215\u003c\/p\u003e \u003cp\u003e14.4 Case Studies 216\u003c\/p\u003e \u003cp\u003e14.5 Conclusions 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Epilogue 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Conclusions 223\u003c\/p\u003e \u003cp\u003e15.2 Open Problems 227\u003c\/p\u003e \u003cp\u003e15.3 Future Directions 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPENDICES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAppendix A Mathematical Foundations 231\u003c\/p\u003e \u003cp\u003eAppendix B Pseudocode of the Basic Algorithms 235\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003eGlossary 259\u003c\/p\u003e \u003cp\u003eIndex 263\u003c\/p\u003e  \u003cp\u003e“Overall, this book presents a valuable framework for further investigation and development for researchers and software developers. Summing Up: Recommended. Graduate students and above.”  (\u003ci\u003eChoice\u003c\/i\u003e, 1 October 2013)\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003ePlamen Parvanov Angelov, Lancaster University, UK\u003c\/strong\u003e\u003cbr\u003ePlamen 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 \u003cem\u003eEvolving Systems\u003c\/em\u003e. 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 \u003cem\u003eComputational Intelligence Magazine\u003c\/em\u003e; \u003cem\u003eAviation Week\u003c\/em\u003e, \u003cem\u003eFlight Global\u003c\/em\u003e, \u003cem\u003eAirframer\u003c\/em\u003e, \u003cem\u003eFlight International\u003c\/em\u003e, 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.   \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAutonomous Learning Systems\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eProviding 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. \u003c\/p\u003e \u003cp\u003eKey features: \u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications.\u003c\/li\u003e \u003cli\u003eCovers a wide range of applications in fields including unmanned vehicles\/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition.\u003c\/li\u003e \u003cli\u003eReviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms.\u003c\/li\u003e \u003cli\u003eAccompanied by a website hosting additional material, including the software toolbox and lecture notes.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eAutonomous 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988782039269,"sku":"NP9781119951520","price":149.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119951520.jpg?v=1761781569","url":"https:\/\/k12savings.com\/products\/autonomous-learning-systems-isbn-9781119951520","provider":"K12savings","version":"1.0","type":"link"}