rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Introduction to fuzzy logic
  name:
    name part: Peckol, James K.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: John Wiley & Sons
    role:
      role term: publisher.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nju
    date issued: 2021
    copyright date: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxxi, 272 pages) : illustrations
  abstract: "Fuzzy logic is finding increased application in the control of real-world processes and in the work with and the manipulation of inexact knowledge. Two of the major attractions of fuzzy logic are: it permits one to express problems in (familiar) linguistic terms and it can be applied where the numerical mathematical model of a system may be too complex or impossible to build using conventional techniques. This book, written in an easily accessible style, assumes that students have a solid background in embedded systems including basic logic design and C/C programming"--
  table of contents: Preface (1-11) Acknowledgements ( 1 ) About the Author ( 1 ) Introduction Chapter 1 A Brief Introduction and History 1 Introduction 1 Models of Human Reasoning 1 The Early Foundation 2 Building On The Past -- From Those Who Laid The Foundation 3 A Learning and Reasoning Taxonomy 4 Rote Learning 4 Learning With a Teacher 5 Learning by Example 5 Analogical or Metaphorical Learning 6 Learning by Problem Solving 6 Learning By Discovery 7 Crisp and Fuzzy Logic 7 Starting To Think Fuzzy 7 History Revisited -- Early Mathematics 9 Foundations of Fuzzy Logic 9 Fuzzy Logic And Approximate Reasoning 9 Non-Monotonic Reasoning 11 Sets and Logic 12 Classical Sets 12 Fuzzy Subsets 13 Fuzzy Membership Functions 14 Expert Systems 16 Summary 17 Review questions 17 Chapter 2 A Review of
  table of contents: Basic Classical Crisp Set Properties 50 Basic Crisp Applications - A First Step 57 Summary 59 Review questions 60 Chapter 4 Fuzzy Sets and Sets and More Sets 61 Introducing Fuzzy 61 Early Mathematics 62 Foundations of Fuzzy Sets Logic 62 Introducing the Basics 64 Introduction to Fuzzy Sets and Set Membership 66 Fuzzy Subsets and Fuzzy Logic 66 Fuzzy Membership Functions 68 Fuzzy Set Theory and Operations 71 Fundamental Terminology 71 Basic Fuzzy Set Properties and Operations 72 Basic Fuzzy Applications - A First Step 83 A Crisp Activity revisited 83 Fuzzy Imprecision and Membership Functions 86 Linear Membership Functions 87 Curved Membership Functions 90 Summary 95 Review questions 96 Chapter 5 What do You Mean by That? 97 Language, Linguistic Variables,
  table of contents: 147 Fuzzy Implication 148 Fuzzy Inference -- Single Premise 149 Max Criterion 150 Mean of Maximum 151 Center of Gravity 152 Fuzzy Inference -- Multiple Premises 153 Getting to work -- Fuzzy Control and Fuzzy Expert Systems 154 Membership Functions 158 System Behavior 159 Defuzzification Strategy 160 Membership Functions 162 System Behavior 163 Defuzzification Strategy 164 Summary 165 Review questions 166 Chapter 8 I Can Do This Stuff !!! 167 Introduction 167 Applications 167 Design Methodology 168 Executing a Design Methodology 169 Summary 172 Review questions 172 Chapter 9 Moving to Threshold Logic !!! 173 Introduction 173 Threshold Logic 173 Executing a Threshold Logic Design 174 Designing an AND Gate 175 Designing an OR Gate 175 Designing a Fundamental Boolean Function
  table of contents: 176 The Downfall of Threshold Logic Design 179 Summary 180 Review Questions 181 Chapter 10 Moving to Perceptron Logic !!! 182 Introduction 182 The Biological Neuron 183 Dissecting the Biological Neuron 184 The Artificial Neuron - A First Step 185 The Perceptron - The Second Step 189 The Basic Perceptron 190 Single and Multilayer Perceptron 192 Bias and Activation Function 193 Learning with Perceptrons - First Step 196 Learning with Perceptrons - The Learning Rule 197 Learning with Perceptrons -Second Step 200 Path of the Perceptron Inputs 201 Testing of the Perceptron 203 Summary 204 Review Questions 205 Appendix A Requirements and Design Specifications 207 Introduction 207 Identifying the requirements 209 Formulating the requirements specification 211 The Environment 212 Characterizing
  note: James K. Peckol.
  note: Includes bibliographical references and index.
  subject:
    topic: Fuzzy logic
  subject:
    topic: Fuzzy sets
  subject:
    topic: Logic, Symbolic and mathematical
  classification: QA9.64 .P43 2021
  classification: 511.3/13
  related item:
    title info:
      title: Introduction to fuzzy logic
    name:
      name part: Peckol, James K..
    origin info:
      publisher: Hoboken, NJ : Wiley, 2021.
    identifier: (DLC) 2021011123
  identifier: 9781119772644
  identifier: 1119772648
  identifier: 111977263X
  identifier: 9781119772620
  identifier: 1119772621
  identifier: 9781119772637
  identifier: 9781119772613
  identifier: 2021011124
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 210310
    record change date: 20260214195720.4
    record identifier: in00004687733
    language of cataloging:
      language term: eng