{"product_id":"introduction-to-fuzzy-logic-isbn-9781119772613","title":"Introduction to Fuzzy Logic","description":"\u003cp\u003e\u003cb\u003eLearn more about the history, foundations, and applications of fuzzy logic in this comprehensive resource by an academic leader\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntroduction to Fuzzy Logic\u003c\/i\u003e delivers a high-level but accessible introduction to the rapidly growing and evolving field of fuzzy logic and its applications. Distinguished engineer, academic, and author James K. Peckol covers a wide variety of practical topics, including the differences between crisp and fuzzy logic, the people and professions who find fuzzy logic useful, and the advantages of using fuzzy logic. \u003c\/p\u003e\u003cp\u003eWhile the book assumes a solid foundation in embedded systems, including basic logic design, and C\/C++ programming, it is written in a practical and easy-to-read style that engages the reader and assists in learning and retention. The author includes introductions of threshold and perceptron logic to further enhance the applicability of the material contained within. \u003c\/p\u003e\u003cp\u003eAfter introducing readers to the topic with a brief description of the history and development of the field, \u003ci\u003eIntroduction to Fuzzy Logic \u003c\/i\u003egoes on to discuss a wide variety of foundational and advanced topics, like: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eA review of Boolean algebra, including logic minimization with algebraic means and Karnaugh maps\u003c\/li\u003e \u003cli\u003eA discussion of crisp sets, including classic set membership, set theory and operations, and basic classical crisp set properties\u003c\/li\u003e \u003cli\u003eA discussion of fuzzy sets, including the foundations of fuzzy sets logic, set membership functions, and fuzzy set properties\u003c\/li\u003e \u003cli\u003eAn analysis of fuzzy inference and approximate reasoning, along with the concepts of containment and entailment and relations between fuzzy subsets\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePerfect for mid-level and upper-level undergraduate and graduate students in electrical, mechanical, and computer engineering courses, \u003ci\u003eIntroduction to Fuzzy Logic\u003c\/i\u003e covers topics included in many artificial intelligence, computational intelligence, and soft computing courses. Math students and professionals in a wide variety of fields will also significantly benefit from the material covered in this book. \u003c\/p\u003e\u003cp\u003ePreface (1-11)\u003c\/p\u003e \u003cp\u003eAcknowledgements ( 1 )\u003c\/p\u003e \u003cp\u003eAbout the Author ( 1 )\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 A Brief Introduction and History 1 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eModels of Human Reasoning 1\u003c\/p\u003e \u003cp\u003eThe Early Foundation 2\u003c\/p\u003e \u003cp\u003eBuilding On The Past - From Those Who Laid The Foundation 3\u003c\/p\u003e \u003cp\u003eA Learning and Reasoning Taxonomy 4\u003c\/p\u003e \u003cp\u003eRote Learning 4\u003c\/p\u003e \u003cp\u003eLearning With a Teacher 5\u003c\/p\u003e \u003cp\u003eLearning by Example 5\u003c\/p\u003e \u003cp\u003eAnalogical or Metaphorical Learning 6\u003c\/p\u003e \u003cp\u003eLearning by Problem Solving 6\u003c\/p\u003e \u003cp\u003eLearning By Discovery 7\u003c\/p\u003e \u003cp\u003eCrisp and Fuzzy Logic 7\u003c\/p\u003e \u003cp\u003eStarting To Think Fuzzy 7\u003c\/p\u003e \u003cp\u003eHistory Revisited - Early Mathematics 9\u003c\/p\u003e \u003cp\u003eFoundations of Fuzzy Logic 9\u003c\/p\u003e \u003cp\u003eFuzzy Logic And Approximate Reasoning 9\u003c\/p\u003e \u003cp\u003eNon-Monotonic Reasoning 11\u003c\/p\u003e \u003cp\u003eSets and Logic 12\u003c\/p\u003e \u003cp\u003eClassical Sets 12\u003c\/p\u003e \u003cp\u003eFuzzy Subsets 13\u003c\/p\u003e \u003cp\u003eFuzzy Membership Functions 14\u003c\/p\u003e \u003cp\u003eExpert Systems 16\u003c\/p\u003e \u003cp\u003eSummary 17\u003c\/p\u003e \u003cp\u003eReview questions 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 A Review of Boolean Algebra 19 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to crisp logic and Boolean Algebra 19\u003c\/p\u003e \u003cp\u003eIntroduction to algebra 20\u003c\/p\u003e \u003cp\u003ePostulates 20\u003c\/p\u003e \u003cp\u003eTheorems 23\u003c\/p\u003e \u003cp\u003eGetting some practice 24\u003c\/p\u003e \u003cp\u003eGetting to work 24\u003c\/p\u003e \u003cp\u003eBoolean Algebra 24\u003c\/p\u003e \u003cp\u003eImplementation 28\u003c\/p\u003e \u003cp\u003eLogic minimization 29\u003c\/p\u003e \u003cp\u003eAlgebraic Means 29\u003c\/p\u003e \u003cp\u003eKarnaugh Maps 30\u003c\/p\u003e \u003cp\u003eApplying the K-map 30\u003c\/p\u003e \u003cp\u003e2 Variable K-Maps 31\u003c\/p\u003e \u003cp\u003e3 Variable K-Maps 32\u003c\/p\u003e \u003cp\u003e4 Variable K-Maps 33\u003c\/p\u003e \u003cp\u003eGoing Backwards 33\u003c\/p\u003e \u003cp\u003eDon’t Care Variables 35\u003c\/p\u003e \u003cp\u003eSummary 37\u003c\/p\u003e \u003cp\u003eReview questions 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Crisp Sets and Sets and More Sets 38 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing the Basics 38\u003c\/p\u003e \u003cp\u003eIntroduction to Classic Sets and Set Membership 41\u003c\/p\u003e \u003cp\u003eClassic Sets 41\u003c\/p\u003e \u003cp\u003eSet Membership 41\u003c\/p\u003e \u003cp\u003eBasic Classic Crisp Set Properties 45\u003c\/p\u003e \u003cp\u003eExploring Sets and Set Membership 46\u003c\/p\u003e \u003cp\u003eFundamental Terminology 47\u003c\/p\u003e \u003cp\u003eElementary Vocabulary 47\u003c\/p\u003e \u003cp\u003eClassical Set Theory and Operations 49\u003c\/p\u003e \u003cp\u003eClassic Set Logic 49\u003c\/p\u003e \u003cp\u003eBasic Classical Crisp Set Properties 50\u003c\/p\u003e \u003cp\u003eBasic Crisp Applications – A First Step 57\u003c\/p\u003e \u003cp\u003eSummary 59\u003c\/p\u003e \u003cp\u003eReview questions 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Fuzzy Sets and Sets and More Sets 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing Fuzzy 61\u003c\/p\u003e \u003cp\u003eEarly Mathematics 62\u003c\/p\u003e \u003cp\u003eFoundations of Fuzzy Sets Logic 62\u003c\/p\u003e \u003cp\u003eIntroducing the Basics 64\u003c\/p\u003e \u003cp\u003eIntroduction to Fuzzy Sets and Set Membership 66\u003c\/p\u003e \u003cp\u003eFuzzy Subsets and Fuzzy Logic 66\u003c\/p\u003e \u003cp\u003eFuzzy Membership Functions 68\u003c\/p\u003e \u003cp\u003eFuzzy Set Theory and Operations 71\u003c\/p\u003e \u003cp\u003eFundamental Terminology 71\u003c\/p\u003e \u003cp\u003eBasic Fuzzy Set Properties and Operations 72\u003c\/p\u003e \u003cp\u003eBasic Fuzzy Applications – A First Step 83\u003c\/p\u003e \u003cp\u003eA Crisp Activity revisited 83\u003c\/p\u003e \u003cp\u003eFuzzy Imprecision and Membership Functions 86\u003c\/p\u003e \u003cp\u003eLinear Membership Functions 87\u003c\/p\u003e \u003cp\u003eCurved Membership Functions 90\u003c\/p\u003e \u003cp\u003eSummary 95\u003c\/p\u003e \u003cp\u003eReview questions 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 What do You Mean by That? 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLanguage, Linguistic Variables, Sets And Hedges 97\u003c\/p\u003e \u003cp\u003eSymbols And Sounds To Real World Objects 99\u003c\/p\u003e \u003cp\u003eCrisp Sets a Second Look 99\u003c\/p\u003e \u003cp\u003eFuzzy Sets a Second Look 103\u003c\/p\u003e \u003cp\u003eLinguistic Variables 103\u003c\/p\u003e \u003cp\u003eMembership Functions 105\u003c\/p\u003e \u003cp\u003eHedges 106\u003c\/p\u003e \u003cp\u003eSummary 110\u003c\/p\u003e \u003cp\u003eReview questions 111\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 If There Were Four Philosophers 112\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFuzzy Inference And Approximate Reasoning 112\u003c\/p\u003e \u003cp\u003eEquality 113\u003c\/p\u003e \u003cp\u003eContainment And Entailment 116\u003c\/p\u003e \u003cp\u003eRelations Between Fuzzy Subsets 119\u003c\/p\u003e \u003cp\u003eUnion and Intersection 119\u003c\/p\u003e \u003cp\u003eConjunction and Disjunction 121\u003c\/p\u003e \u003cp\u003eConditional Relations 125\u003c\/p\u003e \u003cp\u003eComposition Revisited 127\u003c\/p\u003e \u003cp\u003eMax-Min Composition 128\u003c\/p\u003e \u003cp\u003eMax-Product Composition 130\u003c\/p\u003e \u003cp\u003eInference In Fuzzy Logic 137\u003c\/p\u003e \u003cp\u003eSummary 140\u003c\/p\u003e \u003cp\u003eReview questions 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 So How Do I Use This Stuff? 142 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 142\u003c\/p\u003e \u003cp\u003eFuzzification and Defuzzification 143\u003c\/p\u003e \u003cp\u003eFuzzification 143\u003c\/p\u003e \u003cp\u003eDefuzzification 146\u003c\/p\u003e \u003cp\u003eFuzzy Inference Revisited 147\u003c\/p\u003e \u003cp\u003eFuzzy Implication 148\u003c\/p\u003e \u003cp\u003eFuzzy Inference - Single Premise 149\u003c\/p\u003e \u003cp\u003eMax Criterion 150\u003c\/p\u003e \u003cp\u003eMean of Maximum 151\u003c\/p\u003e \u003cp\u003eCenter of Gravity 152\u003c\/p\u003e \u003cp\u003eFuzzy Inference - Multiple Premises 153\u003c\/p\u003e \u003cp\u003eGetting to work - Fuzzy Control and Fuzzy Expert Systems 154\u003c\/p\u003e \u003cp\u003eMembership Functions 158\u003c\/p\u003e \u003cp\u003eSystem Behavior 159\u003c\/p\u003e \u003cp\u003eDefuzzification Strategy 160\u003c\/p\u003e \u003cp\u003eMembership Functions 162\u003c\/p\u003e \u003cp\u003eSystem Behavior 163\u003c\/p\u003e \u003cp\u003eDefuzzification Strategy 164\u003c\/p\u003e \u003cp\u003eSummary 165\u003c\/p\u003e \u003cp\u003eReview questions 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 I Can Do This Stuff !!! 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 167\u003c\/p\u003e \u003cp\u003eApplications 167\u003c\/p\u003e \u003cp\u003eDesign Methodology 168\u003c\/p\u003e \u003cp\u003eExecuting a Design Methodology 169\u003c\/p\u003e \u003cp\u003eSummary 172\u003c\/p\u003e \u003cp\u003eReview questions 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Moving to Threshold Logic !!! 173 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 173\u003c\/p\u003e \u003cp\u003eThreshold Logic 173\u003c\/p\u003e \u003cp\u003eExecuting a Threshold Logic Design 174\u003c\/p\u003e \u003cp\u003eDesigning an AND Gate 175\u003c\/p\u003e \u003cp\u003eDesigning an OR Gate 175\u003c\/p\u003e \u003cp\u003eDesigning a Fundamental Boolean Function 176\u003c\/p\u003e \u003cp\u003eThe Downfall of Threshold Logic Design 179\u003c\/p\u003e \u003cp\u003eSummary 180\u003c\/p\u003e \u003cp\u003eReview Questions 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Moving to Perceptron Logic !!! 182 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 182\u003c\/p\u003e \u003cp\u003eThe Biological Neuron 183\u003c\/p\u003e \u003cp\u003eDissecting the Biological Neuron 184\u003c\/p\u003e \u003cp\u003eThe Artificial Neuron – A First Step 185\u003c\/p\u003e \u003cp\u003eThe Perceptron – The Second Step 189\u003c\/p\u003e \u003cp\u003eThe Basic Perceptron 190\u003c\/p\u003e \u003cp\u003eSingle and Multilayer Perceptron 192\u003c\/p\u003e \u003cp\u003eBias and Activation Function 193\u003c\/p\u003e \u003cp\u003eLearning with Perceptrons – First Step 196\u003c\/p\u003e \u003cp\u003eLearning with Perceptrons – The Learning Rule 197\u003c\/p\u003e \u003cp\u003eLearning with Perceptrons –Second Step 200\u003c\/p\u003e \u003cp\u003ePath of the Perceptron Inputs 201\u003c\/p\u003e \u003cp\u003eTesting of the Perceptron 203\u003c\/p\u003e \u003cp\u003eSummary 204\u003c\/p\u003e \u003cp\u003eReview Questions 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Requirements and Design Specifications 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 207\u003c\/p\u003e \u003cp\u003eIdentifying the requirements 209\u003c\/p\u003e \u003cp\u003eFormulating the requirements specification 211\u003c\/p\u003e \u003cp\u003eThe Environment 212\u003c\/p\u003e \u003cp\u003eCharacterizing External Entities 212\u003c\/p\u003e \u003cp\u003eThe System 213\u003c\/p\u003e \u003cp\u003eCharacterizing the System 214\u003c\/p\u003e \u003cp\u003eSystem Inputs And Outputs 214\u003c\/p\u003e \u003cp\u003eFunctional View 215\u003c\/p\u003e \u003cp\u003eOperational View 215\u003c\/p\u003e \u003cp\u003eTechnological View 215\u003c\/p\u003e \u003cp\u003eSafety, Security, And Reliability 216\u003c\/p\u003e \u003cp\u003eThe System Design Specification 223\u003c\/p\u003e \u003cp\u003eThe System 225\u003c\/p\u003e \u003cp\u003eQuantifying the System 225\u003c\/p\u003e \u003cp\u003eSystem Requirements Versus System Design Specifications 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Introduction to UML 237 \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 237\u003c\/p\u003e \u003cp\u003eUse Cases 238\u003c\/p\u003e \u003cp\u003eWriting a Use Case 240\u003c\/p\u003e \u003cp\u003eClass Diagrams 241\u003c\/p\u003e \u003cp\u003eClass Relationships 242\u003c\/p\u003e \u003cp\u003eInheritance or Generalization 242\u003c\/p\u003e \u003cp\u003eInterface 243\u003c\/p\u003e \u003cp\u003eContainment 243\u003c\/p\u003e \u003cp\u003eAggregation 243\u003c\/p\u003e \u003cp\u003eComposition 244\u003c\/p\u003e \u003cp\u003eDynamic Modeling with UML 245\u003c\/p\u003e \u003cp\u003eInteraction Diagrams 245\u003c\/p\u003e \u003cp\u003eCall and Return 246\u003c\/p\u003e \u003cp\u003eCreate and Destroy 246\u003c\/p\u003e \u003cp\u003eSend 247\u003c\/p\u003e \u003cp\u003eSequence diagrams 247\u003c\/p\u003e \u003cp\u003eFork and join 248\u003c\/p\u003e \u003cp\u003eBranch and merge 249\u003c\/p\u003e \u003cp\u003eActivity diagram 250\u003c\/p\u003e \u003cp\u003eState chart diagrams 251\u003c\/p\u003e \u003cp\u003eEvents 251\u003c\/p\u003e \u003cp\u003eState Machines and State Chart Diagrams 252\u003c\/p\u003e \u003cp\u003eUML State Chart Diagrams 252\u003c\/p\u003e \u003cp\u003eTransitions 253\u003c\/p\u003e \u003cp\u003eGuard Conditions 253\u003c\/p\u003e \u003cp\u003eComposite States 254\u003c\/p\u003e \u003cp\u003eSequential States 254\u003c\/p\u003e \u003cp\u003eHistory States 255\u003c\/p\u003e \u003cp\u003eConcurrent Substates 255\u003c\/p\u003e \u003cp\u003eData Source \/ Sink 256\u003c\/p\u003e \u003cp\u003eData Store 256\u003c\/p\u003e \u003cp\u003ePreparing for Test 258\u003c\/p\u003e \u003cp\u003eThinking Test 258\u003c\/p\u003e \u003cp\u003eExamining the Environment 259\u003c\/p\u003e \u003cp\u003eTest Equipment 259\u003c\/p\u003e \u003cp\u003eThe Eye Diagram 260\u003c\/p\u003e \u003cp\u003eGenerating the Eye Diagram 260\u003c\/p\u003e \u003cp\u003eInterpreting the Eye Diagram 261\u003c\/p\u003e \u003cp\u003eBack of the Envelope Examination 262\u003c\/p\u003e \u003cp\u003eA First Step Check List 262\u003c\/p\u003e \u003cp\u003eRouting and Topology 263\u003c\/p\u003e \u003cp\u003eSummary 263\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJames K. Peckol, PhD, \u003c\/b\u003eis Principal Lecturer Emeritus in the Department of Electrical and Computer Engineering at the University of Washington in Seattle. He has over 50 years of experience in engineering and education in the fields of software, digital, medical, and embedded systems design and development.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLearn more about the history, foundations, and applications of fuzzy logic in this comprehensive resource by an academic leader\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntroduction to Fuzzy Logic\u003c\/i\u003e delivers a high-level but accessible introduction to the rapidly growing and evolving field of fuzzy logic and its applications. Distinguished engineer, academic, and author James K. Peckol covers a wide variety of practical topics, including the differences between crisp and fuzzy logic, the people and professionals who find fuzzy logic useful, and the advantages of using fuzzy logic. \u003c\/p\u003e\u003cp\u003eWhile the book assumes a solid foundation in embedded systems, including basic logic design, and C\/C++ programming, it is written in a practical and easy-to-read style that engages the reader and assists in learning and retention. The author includes introductions of threshold and perceptron logic to further enhance the applicability of the material contained within. \u003c\/p\u003e\u003cp\u003eAfter introducing readers to the topic with a brief description of the history and development of the field, \u003ci\u003eIntroduction to Fuzzy Logic\u003c\/i\u003e goes on to discuss a wide variety of foundational and advanced topics, like: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA review of Boolean algebra, including logic minimization with algebraic means and Karnaugh maps\u003c\/li\u003e \u003cli\u003eA discussion of crisp sets, including classic set membership, set theory and operations, and basic classical crisp set properties\u003c\/li\u003e \u003cli\u003eA discussion of fuzzy sets, including the foundations of fuzzy set logic, set membership functions, and fuzzy set properties\u003c\/li\u003e \u003cli\u003eAn analysis of fuzzy inference and approximate reasoning, along with the concepts of containment and entailment and relations between fuzzy subsets\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for mid-level and upper-level undergraduate and graduate students in electrical, mechanical, and computer engineering courses, \u003ci\u003eIntroduction to Fuzzy Logic\u003c\/i\u003e covers topics included in many artificial intelligence, computational intelligence, and soft computing courses. Math students and professionals in a wide variety of fields will also significantly benefit from the material covered in this book.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989460205797,"sku":"NP9781119772613","price":134.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119772613.jpg?v=1761784188","url":"https:\/\/k12savings.com\/products\/introduction-to-fuzzy-logic-isbn-9781119772613","provider":"K12savings","version":"1.0","type":"link"}