โ Scribed by Plamen Angelov, Dimitar P. Filev, Nik Kasabov
No coin nor oath required. For personal study only.
From theory to techniques, the first all-in-one resource for EISThere is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications. Explains the following fundamental approaches for developing evolving intelligent systems (EIS):the Hierarchical Prioritized Structurethe Participatory Learning Paradigmthe Evolving Takagi-Sugeno fuzzy systems (eTS+)the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithmEmphasizes the importance and increased interest in online processing of data streams Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulationPresents a methodology for developing robust and interpretable evolving fuzzy rule-based systemsIntroduces an integrated approach to incremental (real-time) feature extraction and classificationProposes a study on the stability of evolving neuro-fuzzy recurrent networksDetails methodologies for evolving clustering and classificationReveals different applications of EIS to address real problems in areas of:evolving inferential sensors in chemical and petrochemical industrylearning and recognition in roboticsFeatures downloadable software resources Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.
EVOLVING INTELLIGENT SYSTEMS......Page 5
CONTENTS......Page 7
PREFACE......Page 9
1 LEARNING METHODS FOR EVOLVING INTELLIGENT SYSTEMS......Page 19
2 EVOLVING TAKAGI-SUGENO FUZZY SYSTEMS FROM STREAMING DATA (eTS+)......Page 39
3 FUZZY MODELS OF EVOLVABLE GRANULARITY......Page 69
4 EVOLVING FUZZY MODELING USING PARTICIPATORY LEARNING......Page 85
5 TOWARD ROBUST EVOLVING FUZZY SYSTEMS......Page 105
6 BUILDING INTERPRETABLE SYSTEMS IN REAL TIME......Page 145
7 ONLINE FEATURE EXTRACTION FOR EVOLVING INTELLIGENT SYSTEMS......Page 169
8 STABILITY ANALYSIS FOR AN ONLINE EVOLVING NEURO-FUZZY RECURRENT NETWORK......Page 191
9 ONLINE IDENTIFICATION OF SELF-ORGANIZING FUZZY NEURAL NETWORKS FOR MODELING TIME-VARYING COMPLEX SYSTEMS......Page 219
10 DATA FUSION VIA FISSION FOR THE ANALYSIS OF BRAIN DEATH......Page 247
11 SIMILARITY ANALYSIS AND KNOWLEDGE ACQUISITION BY USE OF EVOLVING NEURAL MODELS AND FUZZY DECISION......Page 265
12 AN EXTENDED VERSION OF THE GUSTAFSON-KESSEL ALGORITHM FOR EVOLVING DATA STREAM CLUSTERING......Page 291
13 EVOLVING FUZZY CLASSIFICATION OF NONSTATIONARY TIME SERIES......Page 319
14 EVOLVING INFERENTIAL SENSORS IN THE CHEMICAL PROCESS INDUSTRY......Page 331
15 RECOGNITION OF HUMAN GRASP BY FUZZY MODELING......Page 355
16 EVOLUTIONARY ARCHITECTURE FOR LIFELONG LEARNING AND REAL-TIME OPERATION IN AUTONOMOUS ROBOTS......Page 383
17 APPLICATIONS OF EVOLVING INTELLIGENT SYSTEMS TO OIL AND GAS INDUSTRY......Page 419
Epilogue......Page 441
About the Editors......Page 443
About the Contributors......Page 445
Index......Page 457
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