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E-raamat: Introduction to Fuzzy Logic [Wiley Online]

(University of Washington)
  • Formaat: 304 pages
  • Ilmumisaeg: 26-Aug-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119772648
  • ISBN-13: 9781119772644
Teised raamatud teemal:
  • Wiley Online
  • Hind: 132,16 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Introduction to Fuzzy LogicSuurem pilt
  • Formaat: 304 pages
  • Ilmumisaeg: 26-Aug-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119772648
  • ISBN-13: 9781119772644
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119772644
"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"--

INTRODUCTION TO FUZZY LOGIC

Learn more about the history, foundations, and applications of fuzzy logic in this comprehensive resource by an academic leader

Introduction to Fuzzy Logic 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.

While 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.

After introducing readers to the topic with a brief description of the history and development of the field, Introduction to Fuzzy Logic goes on to discuss a wide variety of foundational and advanced topics, like:

  • A review of Boolean algebra, including logic minimization with algebraic means and Karnaugh maps
  • A discussion of crisp sets, including classic set membership, set theory and operations, and basic classical crisp set properties
  • A discussion of fuzzy sets, including the foundations of fuzzy set logic, set membership functions, and fuzzy set properties
  • An analysis of fuzzy inference and approximate reasoning, along with the concepts of containment and entailment and relations between fuzzy subsets

Perfect for mid-level and upper-level undergraduate and graduate students in electrical, mechanical, and computer engineering courses, Introduction to Fuzzy Logic 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.

Preface xiii
Acknowledgments xxi
About the Author xxiii
Introduction xxv
1 A Brief Introduction and History
1(18)
1.1 Introduction
1(1)
1.2 Models of Human Reasoning
2(1)
1.2.1 The Early Foundation
2(1)
1.2.1.1 Three Laws of Thought
3(1)
1.3 Building on the Past - From Those Who Laid the Foundation
3(1)
1.4 A Learning and Reasoning Taxonomy
4(3)
1.4.1 Rote Learning
4(1)
1.4.2 Learning with a Teacher
5(1)
1.4.3 Learning by Example
5(1)
1.4.4 Analogical or Metaphorical Learning
6(1)
1.4.5 Learning by Problem Solving
6(1)
1.4.6 Learning by Discovery
6(1)
1.5 Crisp and Fuzzy Logic
7(1)
1.6 Starting to Think Fuzzy
7(1)
1.7 History Revisited - Early Mathematics
8(4)
1.7.1 Foundations of Fuzzy Logic
9(1)
1.7.2 Fuzzy Logic and Approximate Reasoning
9(1)
1.7.3 Non-monotonic Reasoning
10(2)
1.8 Sets and Logic
12(4)
1.8.1 Classical Sets
12(1)
1.8.2 Fuzzy Subsets
13(1)
1.8.3 Fuzzy Membership Functions
13(3)
1.9 Expert Systems
16(1)
1.10 Summary
16(1)
Review Questions
17(2)
2 A Review of Boolean Algebra
19(24)
2.1 Introduction to Crisp Logic and Boolean Algebra
19(1)
2.2 Introduction to Algebra
20(4)
2.2.1 Postulates
20(3)
2.2.2 Theorems
23(1)
2.3 Getting Some Practice
24(1)
2.4 Getting to Work
25(3)
2.4.1 Boolean Algebra
25(1)
2.4.1.1 Operands
25(1)
2.4.1.2 Operators
25(1)
2.4.1.3 Relations
25(3)
2.5 Implementation
28(2)
2.6 Logic Minimization
30(10)
2.6.1 Algebraic Means
30(1)
2.6.2 Karnaugh Maps
31(1)
2.6.2.1 Applying the K-Map
31(1)
2.6.2.2 Two-Variable K-Maps
32(1)
2.6.2.3 Three-Variable K-Maps
33(2)
2.6.2.4 Four-Variable K-Maps
35(1)
2.6.2.5 Going Backward
36(2)
2.6.2.6 Don't Care Variables
38(2)
2.7 Summary
40(1)
Review Questions
41(2)
3 Crisp Sets and Sets and More Sets
43(20)
3.1 Introducing the Basics
43(3)
3.2 Introduction to Classic Sets and Set Membership
46(7)
3.2.1 Classic Sets
46(1)
3.2.2 Set Membership
46(3)
3.2.3 Set Operations
49(2)
3.2.4 Exploring Sets and Set Membership
51(1)
3.2.5 Fundamental Terminology
51(1)
3.2.6 Elementary Vocabulary
51(2)
3.3 Classical Set Theory and Operations
53(7)
3.3.1 Classical Set Logic
53(1)
3.3.2 Basic Classic Crisp Set Properties
54(6)
3.4 Basic Crisp Applications - A First Step
60(1)
3.5 Summary
61(1)
Review Questions
61(2)
4 Fuzzy Sets and Sets and More Sets
63(38)
4.1 Introducing Fuzzy
63(1)
4.2 Early Mathematics
64(1)
4.3 Foundations of Fuzzy Logic
64(2)
4.4 Introducing the Basics
66(2)
4.5 Introduction to Fuzzy Sets and Set Membership
68(2)
4.5.1 Fuzzy Subsets and Fuzzy Logic
68(2)
4.6 Fuzzy Membership Functions
70(3)
4.7 Fuzzy Set Theory and Operations
73(12)
4.7.1 Fundamental Terminology
73(1)
4.7.2 Basic Fuzzy Set Properties and Operations
73(12)
4.8 Basic Fuzzy Applications - A First Step
85(3)
4.8.1 A Crisp Activity Revisited 85
4(84)
4.9 Fuzzy Imprecision And Membership Functions
88(10)
4.9.1 Linear Membership Functions
89(3)
4.9.2 Curved Membership Functions
92(6)
4.10 Summary
98(1)
Review Questions
98(3)
5 What Do You Mean By That?
101(16)
5.1 Language, Linguistic Variables, Sets, and Hedges
101(3)
5.2 Symbols and Sounds to Real-World Objects
104(6)
5.2.1 Crisp Sets - a Second Look
104(4)
5.2.2 Fuzzy Sets - a Second Look
108(1)
5.2.2.1 Linguistic Variables
108(2)
5.2.2.2 Membership Functions
110(1)
5.3 Hedges
110(4)
5.4 Summary
114(1)
Review Questions
115(2)
6 If There Are Four Philosophers
117(26)
6.1 Fuzzy Inference and Approximate Reasoning
117(1)
6.2 Equality
118(3)
6.3 Containment and Entailment
121(3)
6.4 Relations Between Fuzzy Subsets
124(15)
6.4.1 Union and Intersection
124(1)
6.4.1.1 Union
124(1)
6.4.1.2 Intersection
125(1)
6.4.2 Conjunction and Disjunction
126(2)
6.4.3 Conditional Relations
128(2)
6.4.4 Composition Revisited
130(9)
6.5 Inference'in Fuzzy Logic
139(2)
6.6 Summary
141(1)
Review Questions
142(1)
7 So How Do I Use This Stuff?
143(24)
7.1 Introduction
143(1)
7.2 Fuzzification and Denazification
144(4)
7.2.1 Fuzzification
144(3)
7.2.1.1 Graphical Membership Function Features
147(1)
7.2.2 Defuzzification
147(1)
7.3 Fuzzy Inference Revisited
148(2)
7.3.1 Fuzzy Implication
149(1)
7.4 Fuzzy Inference - Single Premise
150(4)
7.4.1 Max Criterion
152(1)
7.4.2 Mean of Maximum
152(1)
7.4.3 Center of Gravity
153(1)
7.5 Fuzzy Inference - Multiple Premises
154(1)
7.6 Getting to Work - Fuzzy Control and Fuzzy Expert Systems
155(10)
7.6.1 System Behavior
159(1)
7.6.2 Defuzzification Strategy
160(1)
7.6.2.1 Test Case
160(2)
7.6.3 Membership Functions
162(2)
7.6.4 System Behavior
164(1)
7.6.4.1 Defuzzification Strategy
164(1)
1.1 Summary
165(1)
Review Questions
165(2)
8 I Can Do This Stuff!!!
167(6)
8.1 Introduction
167(1)
8.2 Applications
167(1)
8.3 Design Methodology
168(1)
8.4 Executing a Design Methodology
169(3)
8.5 Summary
172(1)
Review Questions
172(1)
9 Moving to Threshold Logic!!!
173(10)
9.1 Introduction
173(1)
9.2 Threshold Logic
174(1)
9.3 Executing a Threshold Logic Design
175(4)
9.3.1 Designing an AND Gate
175(1)
9.3.2 Designing an OR Gate
176(1)
9.3.3 Designing a Fundamental Boolean Function
176(3)
9.4 The Downfall of Threshold Logic Design
179(1)
9.5 Summary
180(1)
Review Questions
180(3)
10 Moving to Perceptron Logic !!!
183(26)
10.1 Introduction
183(1)
10.2 The Biological Neuron
184(2)
10.2.1 Dissecting the Biological Neuron
185(1)
10.2.1.1 Dendrites
185(1)
10.2.1.2 Cell Body - Soma
185(1)
10.2.1.3 Axon - Myelin Sheath
185(1)
10.2.1.4 Synapse
186(1)
10.3 The Artificial Neuron - a First Step
186(5)
10.4 The Perceptron - The Second Step
191(7)
10.4.1 The Basic Perceptron
192(2)
10.4.2 Single-and Multilayer Perceptron
194(1)
10.4.3 Bias and Activation Function
195(3)
10.5 Learning with Perceptrons - First Step
198(4)
10.5.1 Learning with Perceptrons - The Learning Rule
200(2)
10.6 Learning with Perceptrons Second Step
202(3)
10.6.1 Path of the Perceptron Inputs
202(2)
10.6.1.1 Implementation/Execution Concerns
204(1)
10.7 Testing of the Perceptron
205(1)
10.8 Summary
206(1)
Review Questions
207(2)
A Requirements and Design Specification
209(24)
A.1 Introduction
209(2)
A.2 Identifying the Requirements
211(2)
A.3 Formulating the Requirements Specification
213(7)
A.3.1 The Environment
214(1)
A.3.1.1 Characterizing External Entities
214(1)
A.3.2 The System
215(1)
A.3.2.1 Characterizing the System
216(4)
A.4 The System Design Specification
220(11)
A.4.1 The System
222(1)
A.4.2 Quantifying the System
222(9)
A.5 System Requirements Versus System Design Specifications
231(2)
B Introduction to UML and Thinking Test
233(24)
B.1 Introduction
233(1)
B.2 Use Cases
234(2)
B.2.1 Writing a Use Case
236(1)
B.3 Class Diagrams
236(3)
B.3.1 Class Relationships
237(1)
B.3.1.1 Inheritance or Generalization
237(1)
B.3.1.2 Interface
238(1)
B.3.1.3 Containment
238(1)
B.3.1.4 Aggregation
238(1)
B.3.1.5 Composition
239(1)
B.4 Dynamic Modeling with UML
239(1)
B.5 Interaction Diagrams
240(1)
B.5.1 Call and Return
240(1)
B.5.2 Create and Destroy
241(1)
B.5.2.1 Send
241(1)
B.6 Sequence Diagrams
241(2)
B.7 Fork and Join
243(1)
B.8 Branch and Merge
244(1)
B.9 Activity Diagram
244(1)
B.10 State Chart Diagrams
245(6)
B.10.1 Events
245(1)
B.10.2 State Machines and State Chart Diagrams
246(1)
B.10.2.1 UML State Chart Diagrams
246(1)
B.10.2.2 Transitions
246(1)
B.10.2.3 Guard Conditions
246(2)
B.10.2.4 Composite States
248(1)
B.10.2.5 Sequential States
248(1)
B.10.2.6 History States
248(1)
B.10.2.7 Concurrent Substates
249(1)
B.10.2.8 Data Source/Sink
249(1)
B.10.2.9 Data Store
249(2)
B.11 Preparing for Test
251(4)
B.11.1 Thinking Test
251(1)
B.11.2 Examining the Environment
252(1)
B.11.2.1 Test Equipment
252(1)
B.11.2.2 The Eye Diagram
253(1)
B.11.2.3 Generating the Eye Diagram
253(1)
B.11.2.4 Interpreting the Eye Diagram
254(1)
B.11.3 Back of the Envelope Examination
255(1)
B.11.3.1 A First Step Check List
255(1)
B.11.4 Routing and Topology
255(1)
B.12 Summary
255(2)
Bibliography 257(6)
Index 263
James K. Peckol, PhD, is 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.
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