{"product_id":"fuzzy-logic-with-engineering-applications-isbn-9781119235866","title":"Fuzzy Logic with Engineering Applications","description":"\u003cp\u003e\u003cb\u003eExplore the diverse electrical engineering application of polymer composite materials with this in-depth collection edited by leaders in the field\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003e\u003ci\u003ePolymer Composites for Electrical Engineering\u003c\/i\u003e delivers a comprehensive exploration of the fundamental principles, state-of-the-art research, and future challenges of polymer composites. Written from the perspective of electrical engineering applications, like electrical and thermal energy storage, high temperature applications, fire retardance, power cables, electric stress control, and others, the book covers all major application branches of these widely used materials.      \u003c\/p\u003e\u003cp\u003eRather than focus on polymer composite materials themselves, the distinguished editors have chosen to collect contributions from industry leaders in the area of real and practical electrical engineering applications of polymer composites. The book�s relevance will only increase as advanced polymer composites receive more attention and interest in the area of advanced electronic devices and electric power equipment.      \u003c\/p\u003e\u003cp\u003eUnique amongst its peers, Polymer Composites for Electrical Engineering offers readers a collection of practical and insightful materials that will be of great interest to both academic and industrial audiences. Those resources include:      \u003c\/p\u003e\u003cli\u003eA comprehensive discussion of glass fiber reinforced polymer composites for power equipment, including GIS, bushing, transformers, and more)   \u003c\/li\u003e\u003cli\u003eExplorations of polymer composites for capacitors, outdoor insulation, electric stress control, power cable insulation, electrical and thermal energy storage, and high temperature applications   \u003c\/li\u003e\u003cli\u003eA treatment of semi-conductive polymer composites for power cables   \u003c\/li\u003e\u003cli\u003eIn-depth analysis of fire-retardant polymer composites for electrical engineering   \u003c\/li\u003e\u003cli\u003eAn examination of polymer composite conductors      \u003cp\u003ePerfect for postgraduate students and researchers working in the fields of electrical, electronic, and polymer engineering, \u003ci\u003ePolymer Composites for Electrical Engineering\u003c\/i\u003e will also earn a place in the libraries of those working in the areas of composite materials, energy science and technology, and nanotechnology. \u003c\/p\u003e\n\u003cp\u003eAbout the Author xi\u003c\/p\u003e \u003cp\u003ePreface to the Fourth Edition xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Case for Imprecision 2\u003c\/p\u003e \u003cp\u003eA Historical Perspective 4\u003c\/p\u003e \u003cp\u003eThe Utility of Fuzzy Systems 7\u003c\/p\u003e \u003cp\u003eLimitations of Fuzzy Systems 9\u003c\/p\u003e \u003cp\u003eThe Illusion: Ignoring Uncertainty and Accuracy 11\u003c\/p\u003e \u003cp\u003eUncertainty and Information 13\u003c\/p\u003e \u003cp\u003eFuzzy Sets and Membership 14\u003c\/p\u003e \u003cp\u003eChance versus Fuzziness 17\u003c\/p\u003e \u003cp\u003eIntuition of Uncertainty: Fuzzy versus Probability 19\u003c\/p\u003e \u003cp\u003eSets as Points in Hypercubes 21\u003c\/p\u003e \u003cp\u003eSummary 23\u003c\/p\u003e \u003cp\u003eReferences 23\u003c\/p\u003e \u003cp\u003eProblems 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Classical Sets and Fuzzy Sets 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClassical Sets 28\u003c\/p\u003e \u003cp\u003eFuzzy Sets 36\u003c\/p\u003e \u003cp\u003eSummary 45\u003c\/p\u003e \u003cp\u003eReferences 46\u003c\/p\u003e \u003cp\u003eProblems 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Classical Relations and Fuzzy Relations 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCartesian Product 52\u003c\/p\u003e \u003cp\u003eCrisp Relations 53\u003c\/p\u003e \u003cp\u003eFuzzy Relations 58\u003c\/p\u003e \u003cp\u003eTolerance and Equivalence Relations 67\u003c\/p\u003e \u003cp\u003eFuzzy Tolerance and Equivalence Relations 70\u003c\/p\u003e \u003cp\u003eValue Assignments 72\u003c\/p\u003e \u003cp\u003eOther Forms of the Composition Operation 76\u003c\/p\u003e \u003cp\u003eSummary 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003eProblems 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Properties of Membership Functions, Fuzzification, and Defuzzification 84\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFeatures of the Membership Function 85\u003c\/p\u003e \u003cp\u003eVarious Forms 87\u003c\/p\u003e \u003cp\u003eFuzzification 88\u003c\/p\u003e \u003cp\u003eDefuzzification to Crisp Sets 90\u003c\/p\u003e \u003cp\u003eλ-Cuts for Fuzzy Relations 92\u003c\/p\u003e \u003cp\u003eDefuzzification to Scalars 93\u003c\/p\u003e \u003cp\u003eSummary 102\u003c\/p\u003e \u003cp\u003eReferences 103\u003c\/p\u003e \u003cp\u003eProblems 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Logic and Fuzzy Systems 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart I: Logic 107\u003c\/p\u003e \u003cp\u003eClassical Logic 108\u003c\/p\u003e \u003cp\u003eFuzzy Logic 122\u003c\/p\u003e \u003cp\u003ePart II: Fuzzy Systems 132\u003c\/p\u003e \u003cp\u003eSummary 151\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003eProblems 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Historical Methods of Developing Membership Functions 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMembership Value Assignments 164\u003c\/p\u003e \u003cp\u003eIntuition 164\u003c\/p\u003e \u003cp\u003eInference 165\u003c\/p\u003e \u003cp\u003eRank Ordering 167\u003c\/p\u003e \u003cp\u003eNeural Networks 168\u003c\/p\u003e \u003cp\u003eGenetic Algorithms 179\u003c\/p\u003e \u003cp\u003eInductive Reasoning 188\u003c\/p\u003e \u003cp\u003eSummary 195\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003eProblems 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Automated Methods for Fuzzy Systems 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefinitions 202\u003c\/p\u003e \u003cp\u003eBatch Least Squares Algorithm 205\u003c\/p\u003e \u003cp\u003eRecursive Least Squares Algorithm 210\u003c\/p\u003e \u003cp\u003eGradient Method 213\u003c\/p\u003e \u003cp\u003eClustering Method 218\u003c\/p\u003e \u003cp\u003eLearning from Examples 221\u003c\/p\u003e \u003cp\u003eModified Learning from Examples 224\u003c\/p\u003e \u003cp\u003eSummary 233\u003c\/p\u003e \u003cp\u003eReferences 235\u003c\/p\u003e \u003cp\u003eProblems 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fuzzy Systems Simulation 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFuzzy Relational Equations 242\u003c\/p\u003e \u003cp\u003eNonlinear Simulation Using Fuzzy Systems 243\u003c\/p\u003e \u003cp\u003eFuzzy Associative Memories (FAMs) 246\u003c\/p\u003e \u003cp\u003eSummary 257\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003eProblems 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Decision Making with Fuzzy Information 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFuzzy Synthetic Evaluation 267\u003c\/p\u003e \u003cp\u003eFuzzy Ordering 269\u003c\/p\u003e \u003cp\u003eNontransitive Ranking 272\u003c\/p\u003e \u003cp\u003ePreference and Consensus 275\u003c\/p\u003e \u003cp\u003eMultiobjective Decision Making 279\u003c\/p\u003e \u003cp\u003eFuzzy Bayesian Decision Method 285\u003c\/p\u003e \u003cp\u003eDecision Making under Fuzzy States and Fuzzy Actions 295\u003c\/p\u003e \u003cp\u003eSummary 309\u003c\/p\u003e \u003cp\u003eReferences 310\u003c\/p\u003e \u003cp\u003eProblems 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Fuzzy Classification and Pattern Recognition 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFuzzy Classification 324\u003c\/p\u003e \u003cp\u003eClassification by Equivalence Relations 324\u003c\/p\u003e \u003cp\u003eCluster Analysis 332\u003c\/p\u003e \u003cp\u003eCluster Validity 332\u003c\/p\u003e \u003cp\u003e\u003ci\u003ec\u003c\/i\u003e-Means Clustering 333\u003c\/p\u003e \u003cp\u003eHard \u003ci\u003ec\u003c\/i\u003e-Means (HCM) 333\u003c\/p\u003e \u003cp\u003eFuzzy \u003ci\u003ec\u003c\/i\u003e-Means (FCM) 343\u003c\/p\u003e \u003cp\u003eClassification Metric 351\u003c\/p\u003e \u003cp\u003eHardening the Fuzzy \u003ci\u003ec\u003c\/i\u003e-Partition 354\u003c\/p\u003e \u003cp\u003eSimilarity Relations from Clustering 356\u003c\/p\u003e \u003cp\u003eFuzzy Pattern Recognition 357\u003c\/p\u003e \u003cp\u003eSingle-Sample Identification 357\u003c\/p\u003e \u003cp\u003eMultifeature Pattern Recognition 365\u003c\/p\u003e \u003cp\u003eSummary 378\u003c\/p\u003e \u003cp\u003eReferences 379\u003c\/p\u003e \u003cp\u003eProblems 380\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Fuzzy Control Systems 388\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eControl System Design Problem 390\u003c\/p\u003e \u003cp\u003eExamples of Fuzzy Control System Design 393\u003c\/p\u003e \u003cp\u003eFuzzy Engineering Process Control 404\u003c\/p\u003e \u003cp\u003eFuzzy Statistical Process Control 417\u003c\/p\u003e \u003cp\u003eIndustrial Applications 431\u003c\/p\u003e \u003cp\u003eSummary 434\u003c\/p\u003e \u003cp\u003eReferences 437\u003c\/p\u003e \u003cp\u003eProblems 438\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Applications of Fuzzy Systems Using Miscellaneous Models 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFuzzy Optimization 455\u003c\/p\u003e \u003cp\u003eFuzzy Cognitive Mapping 462\u003c\/p\u003e \u003cp\u003eAgent-Based Models 477\u003c\/p\u003e \u003cp\u003eFuzzy Arithmetic and the Extension Principle 481\u003c\/p\u003e \u003cp\u003eFuzzy Algebra 487\u003c\/p\u003e \u003cp\u003eData Fusion 491\u003c\/p\u003e \u003cp\u003eSummary 498\u003c\/p\u003e \u003cp\u003eReferences 498\u003c\/p\u003e \u003cp\u003eProblems 500\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Monotone Measures: Belief, Plausibility, Probability, and Possibility 505\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMonotone Measures 506\u003c\/p\u003e \u003cp\u003eBelief and Plausibility 507\u003c\/p\u003e \u003cp\u003eEvidence Theory 512\u003c\/p\u003e \u003cp\u003eProbability Measures 515\u003c\/p\u003e \u003cp\u003ePossibility and Necessity Measures 517\u003c\/p\u003e \u003cp\u003ePossibility Distributions as Fuzzy Sets 525\u003c\/p\u003e \u003cp\u003ePossibility Distributions Derived from Empirical Intervals 528\u003c\/p\u003e \u003cp\u003eSummary 548\u003c\/p\u003e \u003cp\u003eReferences 549\u003c\/p\u003e \u003cp\u003eProblems 550\u003c\/p\u003e \u003cp\u003eIndex 554\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTimothy J. Ross, University of New Mexico, USA\u003cbr\u003e\u003c\/b\u003eDr. Ross is a professor within the Department of Civil Engineering at the University of New Mexico where he teaches courses in structural analysis, structural dynamics and fuzzy logic. He is a registered professional engineer with over 30 years’ experience in the fields of computational mechanics, hazard survivability, structural dynamics, structural safety, stochastic processes, risk assessment, and fuzzy systems. He is also the founding Editor-in-Chief of the International Journal, Intelligent and Fuzzy Systems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eFuzzy Logic with Engineering Applications, Fourth Edition\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTimothy J. Ross, University of New Mexico, USA\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eThe latest update on this popular textbook\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eThe importance of concepts and methods based on fuzzy logic and fuzzy set theory has been rapidly growing since the early 1990s and all the indications are that this trend will continue in the foreseeable future. \u003ci\u003eFuzzy Logic with Engineering Applications, Fourth Edition\u003c\/i\u003e is a new edition of the popular textbook with 15% of new and updated material. Updates have been made to most of the chapters and each chapter now includes new end-of-chapter problems.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNew edition of the popular textbook with 15% of new and updated material.\u003c\/li\u003e \u003cli\u003eIncludes new examples and end-of-chapter problems.\u003c\/li\u003e \u003cli\u003eHas been made more concise with the removal of out of date material.\u003c\/li\u003e \u003cli\u003eCovers applications of fuzzy logic to engineering and science.\u003c\/li\u003e \u003cli\u003eAccompanied by a website hosting a solutions manual and software.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eThe book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.\u003c\/p\u003e\n\u003c\/li\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989268676837,"sku":"NP9781119235866","price":78.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119235866.jpg?v=1761783453","url":"https:\/\/k12savings.com\/products\/fuzzy-logic-with-engineering-applications-isbn-9781119235866","provider":"K12savings","version":"1.0","type":"link"}