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Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications 2009 ed. [Kõva köide]

  • Formaat: Hardback, 400 pages, kõrgus x laius: 235x155 mm, kaal: 1780 g, XLVIII, 400 p., 1 Hardback
  • Sari: Studies in Fuzziness and Soft Computing 240
  • Ilmumisaeg: 08-Apr-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540899235
  • ISBN-13: 9783540899235
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  • Formaat: Hardback, 400 pages, kõrgus x laius: 235x155 mm, kaal: 1780 g, XLVIII, 400 p., 1 Hardback
  • Sari: Studies in Fuzziness and Soft Computing 240
  • Ilmumisaeg: 08-Apr-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540899235
  • ISBN-13: 9783540899235
Teised raamatud teemal:

The objective of this book is to present an uncertainty modeling approach using a new type of fuzzy system model via "Fuzzy Functions". Since most researchers on fuzzy systems are more familiar with the standard fuzzy rule bases and their inference system structures, many standard tools of fuzzy system modeling approaches are reviewed to demonstrate the novelty of the structurally different fuzzy functions, before we introduced the new methodologies. To make the discussions more accessible, no special fuzzy logic and system modeling knowledge is assumed. Therefore, the book itself may be a reference for some related methodologies to most researchers on fuzzy systems analyses. For those readers, who have knowledge of essential fuzzy theories, Chapter 1, 2 should be treated as a review material. Advanced readers ought to be able to read chapters 3, 4 and 5 directly, where proposed methods are presented. Chapter 6 demonstrates experiments conducted on various datasets.



This book presents an uncertainty modeling approach using a new type of fuzzy system model via "Fuzzy Functions". It also reviews standard tools of fuzzy system modeling approaches to demonstrate the novelty of the structurally different fuzzy function.

Arvustused

From the reviews:

The present book has as goal the representation and utilization of uncertainty by means of fuzzy functions. The book begins with a very good overview of the basic notions and principles related to fuzzy sets and systems . The fuzzy models proposed in this book can be used with success by researchers from various domains of activity: engineering, economics, biology, sociology etc., in order to model complex systems. (Ion Iancu, Zentralblatt MATH, Vol. 1168, 2009)

Introduction
1(10)
Motivation
1(2)
Contents of the Book
3(6)
Outline of the Book
9(2)
Fuzzy Sets and Systems
11(40)
Introduction
11(1)
Type-1 Fuzzy Sets and Fuzzy Logic
12(6)
Characteristics of Fuzzy Sets
13(1)
Operations on Fuzzy Sets
14(4)
Fuzzy Logic
18(4)
Structure of Classical Logic Theory
18(1)
Relation of Set and Logic Theory
19(1)
Structure of Fuzzy Logic
19(2)
Approximate Reasoning
21(1)
Fuzzy Relations
22(6)
Operations on Fuzzy Relations
25(1)
Extension Principle
25(3)
Type-2 Fuzzy Sets
28(5)
Type-2 Fuzzy Sets
29(2)
Interval Valued Type-2 Fuzzy Sets
31(1)
Type-2 Fuzzy Set Operations
32(1)
Fuzzy Functions
33(3)
Fuzzy Systems
36(4)
Extensions of Takagi-Sugeno Fuzzy Inference Systems
40(10)
Adaptive-Network-Based Fuzzy Inference System (ANFIS)
41(3)
Dynamically Evolving Neuro-Fuzzy Inference Method (DENFIS)
44(2)
Genetic Fuzzy Systems (GFS)
46(4)
Summary
50(1)
Improved Fuzzy Clustering
51(54)
Introduction
51(1)
Fuzzy Clustering Algorithms
52(12)
Fuzzy C-Means Clustering Algorithm
53(5)
Classification of Objective Based Fuzzy Clustering Algorithms
58(1)
Fuzzy C-Regression Model (FCRM) Clustering Algorithm
58(3)
Variations of Combined Fuzzy Clustering Algorithms
61(3)
Improved Fuzzy Clustering Algorithm (IFC)
64(21)
Motivation
64(5)
Improved Fuzzy Clustering Algorithm for Regression Models (IFC)
69(4)
Improved Fuzzy Clustering Algorithm for Classification Models (IFC-C)
73(4)
Justification of Membership Values of the IFC Algorithm
77(8)
Two New Cluster Validity Indices for IFC and IFC-C
85(18)
Overview of Well-Known Cluster Validity Indices
86(4)
The New Cluster Validity Indices
90(4)
Simulation Experiments [ Celikyilmaz and Turksen, 2007i;2008c]
94(6)
Discussions on Performances of New Cluster Validity Indices Using Simulation Experiments
100(3)
Summary
103(2)
Fuzzy Functions Approach
105(44)
Introduction
105(2)
Motivation
107(5)
Proposed Type-1 Fuzzy Functions Approach Using FCM - T1FF
112(13)
Structure Identification of FF for Regression Models (T1FF)
112(7)
Structure Identification of the Fuzzy Functions for Classification Models (T1FF-C)
119(2)
Inference Mechanism of T1FF for Regression Models
121(1)
Inference Mechanism of T1FF for Classification Models
122(3)
Proposed Type-1 Improved Fuzzy Functions with IFC - T1IFF
125(11)
Structure Identification of T1IFF for Regression Models
125(6)
Structure Identification of T1IFF-C for Classification Models
131(1)
Inference Mechanism of T1IFF for Regression Problems
132(3)
Inference with T1IFF-C for Classification Problems
135(1)
Proposed Evolutionary Type-1 Improved Fuzzy Function Systems
136(11)
Genetic Learning Process: Genetic Tuning of Improved Membership Functions and Improved Fuzzy Functions
139(6)
Inference Method for ET1IFF and ET1IFF-C
145(1)
Reduction of Structure Identification Steps of T1IFF Using the Proposed ET1IFF Method
146(1)
Summary
147(2)
Modeling Uncertainty with Improved Fuzzy Functions
149(68)
Motivation
149(5)
Uncertainty
154(3)
Conventional Type-2 Fuzzy Systems
157(10)
Generalized Type-2 Fuzzy Rule Bases Systems (GT2FRB)
157(3)
Interval Valued Type-2 Fuzzy Rule Bases Systems (IT2FRB)
160(2)
Most Common Type-Reduction Methods
162(2)
Discrete Interval Type-2 Fuzzy Rule Bases (DIT2FRB)
164(3)
Discrete Interval Type-2 Improved Fuzzy Functions
167(26)
Background of Type-2 Improved Fuzzy Functions Approaches
168(11)
Discrete Interval Type-2 Improved Fuzzy Functions System (DIT2IFF)
179(14)
The Advantages of Uncertainty Modeling
193(3)
Discrete Interval Type-2 Improved Fuzzy Functions with Evolutionary Algrithms
196(19)
Motivation
196(1)
Architecture of the Evolutionary Type-2 Improved Fuzzy Functions
197(16)
Reduction of Structure Identification Steps of DIT2IFF Using New EDIT2IFF Method
213(2)
Summary
215(2)
Experiments
217(88)
Experimental Setup
217(10)
Overview of Experiments
217(2)
Three-Way Sub-sampling Cross Validation Method
219(2)
Measuring Models' Prediction Performance
221(1)
Performance Evaluations of Regression Experiments
221(2)
Performance Evaluations of Classification Experiments
223(4)
Parameters of Benchmark Algorithms
227(7)
Support Vector Machines (SVM)
228(1)
Artificial Neural Networks (NN)
229(1)
Adaptive-Network-Based Fuzzy Inference System (ANFIS)
229(2)
Dynamically Evolving Neuro-Fuzzy Inference Method (DENFIS)
231(1)
Discrete Interval Valued Type-2 Fuzzy Rule Base (DIT2FRB)
231(1)
Genetic Fuzzy System (GFS)
232(2)
Logistic Regression, LR, Fuzzy K-Nearest Neighbor, FKNN
234(1)
Parameters of Proposed Fuzzy Functions Algorithms
234(4)
Fuzzy Functions Methods
234(2)
Imporoved Fuzzy Functions Methods
236(2)
Analysis of Experiments - Regression Domain
238(40)
Friedman's Artificial Domain
238(7)
Auto-mileage Dataset
245(6)
Desulphurization Process Dataset
251(11)
Stock Price Analysis
262(14)
Proposed Fuzzy Cluster Validity Index Analysis for Regression
276(2)
Analysis of Experiments - Classification (Pattern Recognition) Domains
278(11)
Classification Datasets from UCI Repository
279(2)
Classification Dataset from StatLib
281(1)
Results from Classification Datasets
281(2)
Proposed Fuzzy Cluster Validity Index Analysis for Classification
283(1)
Performance Comparison Based on Elapsed Times
284(5)
Overall Discussions on Experiments
289(11)
Overall Comparison of System Modeling Methods on Regression Datasets
290(7)
Overall Comparison of System Modeling Methods on Classification Datasets
297(3)
Summary of Results and Discussions
300(5)
Conclusions and Future Work
305(8)
General Conclusions
305(5)
Future Work
310(3)
References
313(8)
Appendix
321
A.1 Set and Logic Theory - Additional Information
321(1)
A.2 Fuzzy Relations (Composition) - An Example
322(1)
B.1 Proof of Fuzzy c-Means Clustering Algorithm
323(3)
B.2 Proof of Improved Fuzzy Clustering Algorithm
326(1)
C.1 Artificial Neural Networks ANNs)
327(2)
C.2 Support Vector Machines
329(9)
C.3 Genetic Algorithms
338(2)
C.4 Multiple Linear Regression Algorithms with Least Squares Estimation
340(1)
C.5 Logistic Regression
341(2)
C.6 Fuzzy K-Nearest Neighbor Approach
343(1)
D.1 T-Test Formula
344(1)
D.2 Friedman's Artificial Dataset: Summary of Results
345(9)
D.3 Auto-mileage Dataset: Summary of Results
354(9)
D.4 Desulphurization Dataset: Summary of Results
363(4)
D.5 Stock Price Datasets: Summary of Results
367(11)
D.6 Classification Datasets: Summary of Results
388(9)
D.7 Cluster Validity Index Graphs
397(1)
D.8 Classification Datasets - ROC Graphs
398