Cao Fuzzy Information and Engineering

Proceedings of the Second International Conference of Fuzzy Information and Engineering (ICFIE)
1. Auflage 2007
ISBN: 978-3-540-71441-5
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 1032 Seiten

Autoren/Hrsg.

Cao, Bing-Yuan

Inhaltsverzeichnis

1;Title Page;1
2;Preface;5
3;Organization;7
4;Contents;10
5;Part I Fuzzy Information;19
6;Contrast Enhancement for Image by WNN and GA Combining PSNR with Information Entropy;20
6.1;Introduction;20
6.2;IBT;21
6.3;Contrast Classification for Image Based on Histogram;22
6.4;Transform Parameters Optimization by GA;22
6.5;IBT Calculation with WNN;25
6.6;Local Contrast Enhancement by Non-linear Operator;26
6.7;Algorithm Steps;27
6.8;Experimental Results;27
6.9;Conclusion;31
6.10;References;31
7;A Generalized Decision Logic Language for Information Tables;33
7.1;Introduction;33
7.2;Decision Logic Language;34
7.3;Generalized Decision Logic Language;36
7.4;Conclusions;38
7.5;References;38
8;New Similarity Measures on Intuitionistic Fuzzy Sets;39
8.1;Introduction;39
8.2;Basic Notions and Definitions of Intuitionistic Fuzzy Sets;40
8.3;New Approaches to Calculating Similarity Measures;41
8.4;Application to Pattern Recognition Problem;44
8.5;Conclusions;46
8.6;References;46
9;Information Source Entropy Based Fuzzy Integral Decision Model on Radiodiagnosis of Congenital Heart Disease;48
9.1;Introduction;48
9.2;Medical Expert Diagnosis System Model;49
9.2.1;Conformation of Attaching Function of Each Symptom;50
9.2.2;Information Analysis of Radiodiagnosis Value and Weight of Each Symptom;52
9.2.3;Fuzzy Integral Decision-Making;53
9.3;Example Analysis and Optimization Processing;54
9.3.1;Example Analysis;54
9.3.2;Optimization Processing;56
9.4;References;57
10;The Design and Research of Controller in Fuzzy PETRI NET;58
10.1;Introduction;58
10.2;The Definition of Fuzzy PETRI NET;58
10.2.1;The Definition of Fuzzy PETRI NET;58
10.2.2;The Rules of Activation;59
10.2.3;Place Invariant;59
10.3;Design of Controller;59
10.4;A Synthetical Algorithm Aiming at the Dead Lock Phenomenon;62
10.5;Dealing with Dead Lock Phenomenon;63
10.6;Summary;65
10.7;References;65
11;Fuzzy Tracing the Source of Net-Virus;67
11.1;Introduction;67
11.2;Source Tracing Modeling;68
11.3;Solutions to the Equations;71
11.4;Simulation Tests;72
11.5;Conclusion and Acknowledgements;74
11.6;References;75
12;Uncertain Temporal Knowledge Reasoning of Train Group Operation Based on Extended Fuzzy-Timing Petri Nets;76
12.1;Introduction;76
12.2; EFTN and Relative Computations;77
12.2.1;Updating Fuzzy Timestamps;77
12.2.2;Possibility Computation;78
12.3;Analysis of Temporal Uncertainty ;78
12.4;Comparing Analysis;79
12.5;Conclusion;80
12.6;References;81
13;An Improved FMM Neural Network for Classification of Gene Expression Data;82
13.1;Introduction;82
13.2;Improving FMM Neural Network Classifier;84
13.2.1;Original FMM Neural Network;84
13.2.2;Drawbacks of FMM Neural Network;85
13.2.3;Improvement of FMM Neural Network;86
13.3;Gene Selection;87
13.4;Experiment Evaluation;89
13.5;Conclusion;90
13.6;References;90
14;Using Assignment Matrix on Incomplete Information Systems Reduction of Attributes;92
14.1;Introduction;92
14.2;Incomplete Information System and Tolerance Relation;93
14.3;Assignment Matrix and Measurement of Conditional Attributes’ Significance to Decision;94
14.4;Assignment Matrix Based Attribute Reduction Algorithm;96
14.5;Example and Analysis;97
14.6;Summary;99
14.7;References;99
15;An Image Compression Algorithm with Controllable Compression Rate;100
15.1;Introduction;100
15.2;The Fuzzy Neural Network;101
15.3;The Technical Details;101
15.4;Performance;103
15.5;Conclusions;104
15.6;References;104
16;A Novel Approach for Fuzzy Connected Image Segmentation;106
16.1;Introduction;106
16.2;Preliminaries;107
16.2.1;A Framework for Fuzzy Connectedness, Relative Fuzzy Connectedness;107
16.2.2;Fuzzy k -Object;109
16.3;Algorithm;109
16.3.1;Original Algorithm;109
16.3.2;Proposed Algorithm;109
16.3.3;Algorithm Complexity Analysis;110
16.4;Experimental and Evaluation ;111
16.4.1;Experimental Result;111
16.4.2;Evaluation;111
16.5;Conculsion;113
16.6;References;114
17;Knowledge Mass and Automatic Reasoning System in Similarity Logic $C_Q$;115
17.1;Introduction and Preliminary;115
17.2;Type V Knowledge Mass, Type V Knowledge Universe, Type V Knowledge Base and Type V $Q$-Automatic Reasoning System;119
17.3;Type V True Level k Knowledge Circle, Extended Type V Knowledge Base and Extended Type V $Q$-Automatic Reasoning System;122
17.4;The Type V Level (k,j) Perfection of Extended Type V Knowledge Base $K^V$;126
17.5;Conclusion;128
17.6;References;128
18;Image Segmentation by Multi-level Thresholding Based on C-Means Clustering Algorithms and Fuzzy Entropy;130
18.1;Introduction;130
18.2;Image Segmentation Based on C-Means Clustering Algorithms;131
18.2.1;C-Means Clustering Algorithms;131
18.2.2;C-Means Clustering Algorithms for Image Segmentation;132
18.3;Perform Post Process Using Fuzzy Entropy;133
18.3.1;Fuzzy Entropy and Membership Function;133
18.3.2;Perform Post Process Using Fuzzy Entropy;133
18.4;Experimental Results;134
18.5;Conclusion;138
18.6;References;138
19;Part II Fuzzy Sets and Systems;139
20;Extension Principle of Interval-Valued Fuzzy Set;140
20.1; Introduction;140
20.2;Preliminaries;141
20.3;Maximal Extension Principle of Interval-Valued Fuzzy Set;144
20.4;Minimal Extension Principle of Interval-Valued Fuzzy Set;146
20.5;Generalized Extension Principle of Interval-Valued Fuzzy Set;150
20.6;Conclusion;151
20.7;References;152
21;On Vague Subring and Its Structure;153
21.1;Introduction;153
21.2;Preliminaries;153
21.3;Vague Ring and Its Substructures;154
21.4;The Homomorphism of Vague Ring;157
21.5;References;158
22;A Common Generalization of Smooth Group and Vague Group;159
22.1;Introduction;159
22.2;Common Generalization of Smooth Group and Vague Group;159
22.3;The Relationships Among Fuzzy Groups Based on Fuzzy Equalities;161
22.4;Conclusion;163
22.5;References;163
23;Sequences of Fuzzy-Valued Choquet Integrable Functions;164
23.1;Introduction;164
23.2;Preliminaries and Propositions;164
23.3;Uniform Integrability, Uniform Absolute Continuity and Uniform Boundedness;166
23.4;Some Convergence Theorems;170
23.5;Conclusions;172
23.6;References;173
24;Generalized Root of Theories in Propositional Fuzzy Logical Systems;174
24.1;Introduction;174
24.2;Preliminaries;175
24.2.1;Logic Systems: ${\L}uk$, $G\ddot{o}d$, $\Pi$ and$L^{\ast}$} \label;175
24.2.2;Generalized Deduction Theorems in ${\L}uk$, $G\ddot{o}d$, $\Pi$ and;177
24.3;Basic Definitions and Properties;178
24.4;Results in the n-Valued ukasiewicz Logic System;180
24.5;Results in the $G\ddot{o}del$ Fuzzy Logic System;181
24.6;Results in the R_o -- Fuzzy Logic System;182
24.7;Conclusion Remarks;183
24.8;References;184
25;Countable Dense Subsets and Countable Nested Sets;185
25.1;Introduction;185
25.2;Countable Decomposition Theorems;186
25.3;Countable Representation Theorems;190
25.4;Conclusion;194
25.5;References;195
26;The Construction of Power Ring;196
26.1;Introduction;196
26.2;Concepts of HX Ring and Power Ring;196
26.3;Construction of HX Ring and Power Ring;198
26.4;References;202
27;On Fuzzy Ideals in BCH-Algebras;203
27.1;Introduction;203
27.2;Preliminaries;203
27.3;Fuzzy Quasi-associate Ideals of BCH-Algebras;204
27.4;References;207
28;Minimization of Lattice Automata;209
28.1;Introduction;209
28.2;Lattice Automata;209
28.3;Refining Congruence and Quotient Automata;210
28.4;Equivalent and Minimal Automata;213
28.5;Minimization Algorithm;215
28.6;An Example;217
28.7;Conclusions;220
28.8;References;220
29;Fixed Points in $M$-Fuzzy Metric Spaces;221
29.1;Introduction;221
29.2;Compatible Mappings of Type (*);224
29.3;Common Fixed Point Theorems;226
29.4;References;229
30;Common Fixed Points of Maps on Intuitionistic Fuzzy Metric Spaces;231
30.1;Introduction;231
30.2;Preliminaries;232
30.3;Result;233
30.4;References;240
31;Generalized Fuzzy $B$-Algebras;241
31.1;Introduction;241
31.2;Preliminary ;241
31.3;($\alpha,\beta$,)-Fuzzy $B$-Algebras;243
31.4;References;248
32;Fuzzy Set Theory Applied to $QS$-Algebras;249
32.1;Introduction;249
32.2;Preliminary;249
32.3;Fuzzy $QS$-Subalgebra;251
32.4;Fuzzy Topological $QS$-Algebra;256
32.5;References;257
33;$L$-Topological Dynamical System and Its Asymptotic Properties;258
33.1;Introduction;258
33.2;Dynamical Systems on F-Lattice;259
33.2.1;Systems;260
33.2.2;Subsystems;262
33.3;Asymptotic Properties;263
33.3.1;Recurrence;263
33.3.2;$ \omega $ -Limit Sets;264
33.3.3;Topological Conjugacy;265
33.4;Conclusion;266
33.5;References;266
34;Integrating Probability and Quotient Space Theory: Quotient Probability;267
34.1;Introduction;267
34.2;Quotient Probability and Some Results;268
34.2.1;Probability Update and Spaces Fusion;269
34.2.2;Distance Between Two Different Quotient Probabilities;271
34.3;Conclusion;273
34.4;References;273
35;.-Convergence Theory of Filters in L.-Spaces;275
35.1;Introduction;275
35.2;Preliminaries;275
35.3;.-Convergence of Filters;277
35.4;Relationships Among .-Convergence of Filters, .-Convergence of Nets and .-Convergence of Ideals;279
35.5;Some Applications of .-Convergence of Filters;281
35.6;References;282
36;On the Perturbation of Fuzzy Matrix Equations with $.$-T Composition;284
36.1;Preliminaries;284
36.2;Fuzzy Solution-Invariant Matrix;286
36.3;Fuzzy Perturbation Issues;289
36.4;References;294
37;Normal Distribution Fuzzy Sets;295
37.1;Introduction;295
37.2;Normal Distribution Fuzzy Sets;296
37.3;Some Properties of Union, Complementation and Intersection;299
37.4;The Relationship Among Fuzzy Sets, Intuitionistic Fuzzy Sets and Normal Distribution Fuzzy Sets;300
37.5;Conclusion;303
37.6;References;303
38;Probabilistic Fuzzy Hypernear-Rings;305
38.1;Introduction ;305
38.2;Preliminaries;306
38.3;Probabilistic Fuzzy Hypernear-Rings;307
38.4;Conclusions;309
38.5;References;310
39;Isomorphic Fuzzy Sets and Fuzzy Approximation Space;311
39.1;Introduction;311
39.2;Isomorphism and Homomorphism of Fuzzy Sets;312
39.3;Fuzzy Sets and Approximation Spaces;314
39.4;Fuzzy Relations and Fuzzy Approximation Spaces;316
39.5;Conclusion;320
39.6;References;321
40;Part III Soft Computing;322
41;Particle Swarm Optimization Algorithm Design for Fuzzy Neural Network;323
41.1;Introduction;323
41.2;Fuzzy Neural Network Architecture;323
41.3;Particle Swarm Optimization with Division of Work;324
41.4;Pruning Algorithm;326
41.5;Numerical Simulations;327
41.6;Conclusion;327
41.7;References;328
42;A Method of Intuitionistic Fuzzy Reasoning Based on Inclusion Degree and Similarity Measure;329
42.1;Introduction;329
42.2;Inclusion Degree Based on Fuzzy Implication Operators;330
42.3;Similarity Measure Between IFSs Based on Inclusion Degree;331
42.4;Intuitionistic Fuzzy Reasoning Based on Similarity Measure;333
42.4.1;Single Rule Case of IFMP;333
42.4.2;Multi-rules Case of IFMP;334
42.4.3;Numerical Example;334
42.5;Conclusion;335
42.6;References;336
43;A Discrete Particle Swarm Optimization Algorithm for the Multiobjective Permutation Flowshop Sequencing Problem;337
43.1;Introduction;337
43.2;The Discrete Particle Swarm Optimization Algorithm for the MPFSP;338
43.2.1;Discrete PSO for the Single Objective PFSP;339
43.2.2;The Proposed DPSO for the MPFSP;339
43.3;Experimental Results;341
43.3.1;The 5 Small Problems with the Objectives of Minimizing the Makespan and the Total Tardiness;341
43.3.2;The 20 Problems of Taillard with the Objectives of Minimizing the Makespan and the Total Flowtime;343
43.4;Conclusions;344
43.5;References;345
44;An Optimization Method for Fuzzy c-Means Algorithm Based on Grid and Density;346
44.1;Introduction;346
44.2;Grid and Density;346
44.3;Initialization Method for Fuzzy c-Means Algorithm Based on Grid and Density;347
44.3.1;Extract Approximate Clustering Center Algorithm Based on Grid and Density;347
44.3.2;Initialization;348
44.4;Experiments;348
44.5;Conclusions;349
44.6;References;350
45;Obstacle Recognition and Collision Avoidance of a Fish Robot Based on Fuzzy Neural Networks;351
45.1;Distance Scanning System for Fish Robots;351
45.2;Distance Scanning System for Fish Robots;352
45.3;Obstacle Recognition System;355
45.3.1;Scanning Obstacles;355
45.3.2;Recognition of Shapes and Estimation of Approaching Angles;356
45.3.3;Experiments;357
45.4;Conclusions;358
45.5;References;358
46;Generalization of Soft Set Theory: From Crisp to Fuzzy Case;359
46.1;Introduction;359
46.2;Theory of Fuzzy Soft Set;360
46.2.1;Soft Set Theory;360
46.2.2;Fuzzy Soft Set Theory;361
46.3;Operators on Fuzzy Soft Set;363
46.3.1;Fuzzy Logic Operators;363
46.3.2;Operators on Fuzzy Soft Set;363
46.4;Application of Fuzzy Soft Set;365
46.5;Conclusion;367
46.6;References;367
47;A New QPSO Based BP Neural Network for Face Detection;369
47.1;Introduction;369
47.2;Quantum Particle Swarm Optimization;370
47.2.1;PSO Algorithm;370
47.2.2;QPSO Algorithm;370
47.3;Improved BPNN Learning Algorithm Based on QPSO;371
47.4;Face Detection Based on the Improved Learning Algorithm;373
47.4.1;Preprocessing;373
47.4.2;Designing the Network;373
47.4.3;Training;373
47.4.4;Detection;374
47.5;Experiment Results;374
47.6;Conclusion;376
47.7;References;377
48;ECC-Based Fuzzy Clustering Algorithm;378
48.1;Introduction;378
48.2;The Hierarchical Model of ECC;379
48.3;ECC-Based Vector Space Model;380
48.4;ECC-Based Fuzzy Clustering Algorithm;382
48.5;Validation;383
48.6; Experimental Results and Analysis;384
48.7;Conclusion;386
48.8;References;386
49;A New Algorithm for Attribute Reduction Based on Discernibility Matrix;387
49.1;Introduction;387
49.2;Basic Concepts;388
49.2.1;Decision Tables and Pawlak Reduction;388
49.2.2;Discernibility Matrices;389
49.3;Algorithms;391
49.3.1;Algorithm Principle;391
49.3.2;Algorithm Describing;392
49.3.3;Complexity Analysis of the Algorithm;393
49.4;Experimental Analysis;393
49.5;Conclusions;394
49.6;References;394
50;Optimal Computing Budget Allocation Based Compound Genetic Algorithm for Large Scale Job Shop Scheduling;396
50.1;Introduction;396
50.2;Job Shop Scheduling Problem Formulation;397
50.3;Heuristic and Search Techniques for Job Shop Scheduling Problem;398
50.4;Genetic Algorithm for Job Shop Scheduling;399
50.4.1;The Encoding Representation and Fitness Value;399
50.4.2;The Selection, Crossover and Mutation Operator;400
50.4.3;Genetic Algorithm Scheme Designing;401
50.5;Optimal Computing Budget Allocation Algorithm for GA Parameters Selection;402
50.5.1;Problem Statement;402
50.5.2;The Method for Algorithm Comparison and Selection;403
50.5.3;Optimal Computing Budget Allocation in Algorithm Comparison;404
50.6;Optimal-Computing-Budget-Allocation Based Compound Genetic Algorithm;406
50.7;Computational Experiments and Analysis;407
50.8;Conclusions;409
50.9;References;409
51;Part IV Fuzzy Engineering;411
52;Multi-Immune-Agent Based Power Quality Monitoring Method for Electrified Railway;412
52.1;Introduction;412
52.2;Immune Agent Model;413
52.3;Immune Supervision Networks Model;413
52.4;Architecture and Algorithm of Immune Agent;414
52.5;Cooperative Multi-Immune-Agent Based Power Quality Monitoring Systems;415
52.5.1;Immune-Terminal Parameter;416
52.5.2;Control Algorithm;418
52.5.3;Control Decision Algorithm;419
52.5.4;Resolving Conflicts;420
52.6;Experiment Evaluation;420
52.6.1;Evaluation;421
52.6.2;Solution to Conflicts;422
52.7;Conclusions;422
52.8;References;422
53;BF Hot Metal Silicon Content Prediction Using Unsupervised Fuzzy Clustering;424
53.1;Introduction;424
53.2;General Scheme and Principle of the Methods;425
53.2.1;Established Temporal Patterns;426
53.2.2;Fuzzy Clustering by State Recognition;426
53.2.3;Fit a Prediction Model to Each Fuzzy Cluster;427
53.2.4;Predicting by a Combination of the Models;427
53.3;Simulation of Predictive Algorithm;427
53.4;Conclusions and Discussion;430
53.5;Refences;430
54;Speech Emotion Pattern Recognition Agent in Mobile Communication Environment Using Fuzzy-SVM;432
54.1;Introduction;432
54.2;Proposed System;433
54.3;Emotional Feature Extractions and Optimization;435
54.3.1;Pre-processing;435
54.3.2;Emotional Feature Extraction;436
54.3.3;SFS Feature Optimization;436
54.3.4;MA (Moving Average) Filter to Minimize Noise Effect;437
54.4;Experimental Results;438
54.4.1;Speech Database and Experimental Setup;438
54.4.2;SFS Feature Optimization Experiment;439
54.4.3;Classification Results for Five Emotional States;439
54.4.4;Classification Results with Two Emotional States;441
54.5;Conclusion;442
54.6;References;442
55;Efficient Wavelet Based Blind Source Separation Algorithm for Dependent Sources;444
55.1;Introduction;444
55.2;BSS Model and Indeterminacies;445
55.3;CWT Based BSS Algorithm for Dependent Sources;446
55.3.1;Problem Statement and Why Use WT;446
55.3.2;How to Use CWT and the Basic Algorithm;448
55.4;CWT Based General BSS Algorithm;449
55.4.1;$m$=2 Mixtures and $n$ Source Signals;449
55.4.2;General Case: $n$ Mixtures and $n$ Source Signals;450
55.5;Procedure of the BSS Algorithm;451
55.6;Simulation Results;452
55.7;Conclusion;453
55.8;References;454
56;An Incentive Mechanism for Peer-to-Peer File Sharing;455
56.1;Introduction;455
56.2;Relation Work;456
56.3;Incentive Mechanism;456
56.4;Experiments;459
56.5;Conclusions and Future Work;460
56.6;References;460
57;Application of the BP Neural Network in the Checking and Controlling Emission of Vehicle Engines;461
57.1;Introduction;461
57.2;Model of the BP Neural Network;461
57.2.1;Construction of the BP Neural Network;462
57.2.2;Equation of the BP Neural Network;462
57.3;Process of the Neural Network Leaning;463
57.4;Application of the BP Neural Network in Checking and Controlling Emission;463
57.5;Result;466
57.6;References;466
58;Oil and Gas Pipeline Limited Charge Optimum Maintenance Decision-Making Analysis Based on Fuzzy-Gray-Element Theory and Fuzzy Analytical Hierarchy Process;468
58.1;Foreword;468
58.2;Theoretical Basis of Fuzzy-Gray-Element;469
58.2.1;Concept of Fuzzy-Gray-Element;469
58.2.2;Correlation Analysis;469
58.3;Fuzzy Hierarchy Analysis Method Assurance the Maintenance Measure Weight;470
58.3.1;The Analytic Hierarchy Process Brief Introduction;470
58.3.2;Maintenance Measure Support Layer Model Establishment;470
58.3.3;Establishment Triangle Fuzzy Number Complementary Judgment Matrix;471
58.3.4;Calculation Triangle Fuzzy Number Weight Vector of Maintenance Factor;471
58.3.5;Calculation Weight of Maintenance Factor;472
58.4;The Fuzzy-Grey-Element Correlative Decision-Making Model;472
58.4.1;The Establishment of the Maintenance Project Decision Model;472
58.4.2;Determination of Optimum Project Based on Fuzzy-Gray-Element Theory;474
58.5;Conclusion;475
58.6;References;476
59;The Fuzzy Seepage Theory Based on Fuzzy Structuring Element Method;477
59.1;Introduction;477
59.2;Fuzzy Structuring Element and Fuzzy Value Function;478
59.2.1;Fuzzy Structuring Element;478
59.2.2;Analysis Expression of Fuzzy Value Function;479
59.3;Establish of Fuzzy Seepage Model;480
59.3.1;Classical Landfill Gas Seepage Model;480
59.3.2;Fuzzy Qualified Differential Equation;481
59.3.3;Fuzzy Seepage Equation;481
59.4;Fuzzy Solution of Fuzzy Seepage Model;482
59.4.1;Analysis Solution of One-Dimension Ideal Seepage Model;482
59.4.2;Expressible Problem of Fuzzy Differential Equation Solution;483
59.4.3;Analysis Expression of Fuzzy Seepage Model;483
59.5;Conclusions;484
59.6;References;484
60;Ecological Safety Comprehensive Evaluation on Mineral-Resource Enterprises Based on AHP;485
60.1;Introduction;485
60.2;Method of Ecological Safety Evaluation for MRE;486
60.2.1;Meaning of Ecological Safety Evaluation for MRE;486
60.2.2;Index System of Ecological Safety Evaluation;486
60.2.3;Method of Evaluating the Ecological Safety of MRE;487
60.3;Example for the Applying of Comprehensive Evaluation on Ecological Safety of MRE;490
60.4;Conclusion;493
60.5;References;493
61;A Note on the Optimal Makespan of a Parallel Machine Scheduling Problem;494
61.1;Introduction;494
61.2;Level Algorithm;495
61.3;The Errors in Proofs for the Optimal Makespan;496
61.3.1;The Error in the Proof of $C_{\textmd{max}}\leq l_{\textmd{max}}+c$ in \cite{Tang};497
61.3.2;The Errors in the Proof of Cmax = lmax + c in [7];498
61.4;A New Algorithm and the Proof for the OptimalMakespan of $P|p_j=1,intree|C_{\textmd{max}}$;501
61.5;Conclusion;506
61.6;References;506
62;Part V Fuzzy Operation Research and Management;507
63;Advances in Fuzzy Geometric Programming;508
63.1;Introduction;508
63.2;Fuzzy Geometric Programming;509
63.3;Present Situation of Fuzzy Geometric Programming;510
63.4;Future Development of Fuzzy Geometric Programming;511
63.5;References;512
64;A Method for Estimating Criteria Weights from Intuitionistic Preference Relations;514
64.1;Introduction;514
64.2;Preliminaries;515
64.3;Consistent Intuitionistic Preference Relation;516
64.4;A Method for Estimating Criteria Weights;517
64.5;Conclusions;521
64.6;References;521
65;The Area Compensation Method About Fuzzy Order and Its Application;524
65.1;Introduction;524
65.2;Basic Concept;525
65.3;Ranking Fuzzy Numbers;526
65.4;Approach to the Fuzzy Linear Programming with Fuzzy Variables ;528
65.5;Extensions for the Area Compensation Ranking Method ;531
65.6; Conclusion ;532
65.7;References;533
66;A Method for the Priority Vector of Fuzzy Reciprocal Matrix;534
66.1;Introduction;534
66.2;Some Properties of Fuzzy Consistent Matrix;535
66.3;Algorithm;539
66.4;Numerical Examples;542
66.5;Conclusion;543
66.6;References;543
67;The Theory of Fuzzy Logic Programming;545
67.1;Introduction;545
67.2;Syntax and Semantics of Fuzzy Horn Clause Logic;545
67.3;Fuzzy Proof Theory;550
67.4;Fuzzy Procedural Interpretation;552
67.5;Conclusions;553
67.6;References;553
68;Multiobjective Matrix Game with Vague Payoffs;554
68.1;Introduction;554
68.2;Definition and Order Function of Vague Set;555
68.3;Model of Multiobjective Two-Person Zero-Sum Matrix Game Based on Vague Set;556
68.4;Solutions of Multiobjective Two-Person Zero-Sum Matrix Game Based on Vague Set;557
68.5;Conclusion;561
68.6;References;561
69;Fuzzy Geometric Object Modelling;562
69.1;Introduction;562
69.2;Modelling Fuzzy Geometric Objects Using Smooth Unit Step Function and Implicit Functions;564
69.2.1;Method 1: Fuzzification of a Solid Geometric Object;565
69.2.2;Method 2: Fuzzification of the Boundary of a Geometric Object;566
69.2.3;Method 3: Adding a Fuzzy Boundary to an Ordinary Solid Geometric Object;567
69.3;Modelling Fuzzy Geometric Objects Using Geometric Meshes and Parametrically Defined Geometric Shapes;568
69.4;Fuzzy Geometric Objects Blending;568
69.5;Shape Preserving Fuzzy Geometric Blending;569
69.6;References;572
70;A Fuzzy Portfolio Selection Methodology Under Investing Constraints;575
70.1;Introduction;575
70.2;Possibilistic Mean and Variance;576
70.3;A Fuzzy Portfolio Selection Model Under Investing Constraints ;578
70.4;Numerical Example;581
70.5;Conclusions;582
70.6;References;583
71;The Interaction Among Players in Fuzzy Games;584
71.1;Introduction;584
71.2;Interaction for Crisp Cooperative Games;585
71.3;Interaction Among Players in Games with Fuzzy Coalitions;586
71.3.1;Fuzzy Coalitions and $LP$-Derivative;586
71.3.2;Mutual Independence Among Levels Based on $s$;588
71.3.3;Interaction Among Levels of $LP(|LP|\geq 2)$ with Respect to Players of $M$;589
71.3.4;The Interaction Among Players of $P(|P|\geq 2)$;590
71.4;Conclusion;593
71.5;References;593
72;Novel Method for Fuzzy Hybrid Multiple Attribute Decision Making;594
72.1;Introduction;594
72.2;The Description for the Problem of Fuzzy Hybrid Multiple Attribute Decision Making;595
72.3;The Principle and Method of the Decision Making;595
72.3.1;Processing Data for Indexes;595
72.3.2;The New Method Based on the Grey Relational Degree;597
72.3.3;The Basic Steps of the New Decision Making Method;599
72.4;An Application Example ;600
72.5;Conclusions;601
72.6;References;602
73;Auto-weighted Horizontal Collaboration Fuzzy Clustering;603
73.1;Introduction;603
73.2;Prelimimary;604
73.2.1;Fuzzy C-Means (FCM);604
73.2.2;Horizontal Collaboration Fuzzy C-Means;604
73.3;The Determining of Weights in HC-FCM;606
73.3.1;Measure of Partition Similarity;606
73.3.2;Encouragement Approach;607
73.3.3;Penalty Approach;609
73.4;Conclusions;610
73.5;References;611
74;A Web-Based Fuzzy Decision Support System for Spare Parts Inventory Control;612
74.1;Introduction;612
74.2;The Framework for SPICDSS;613
74.2.1;The General Integrated Framework;613
74.2.2;The Criticality Class Evaluation Methodology;613
74.2.3;The Web-based Replenishment DSS (WRDSS);616
74.3;The Development of SPICDSS and Application;618
74.3.1;Prototype System;618
74.3.2;Application Analysis;619
74.4;Conclusions;619
74.5;References;619
75;Fuzzy Ranking for Influence Factor of Injury Surveillance Quality in Hospital;621
75.1;Introduction[5][6];621
75.1.1;Factors from the Filling Staff Include;622
75.1.2;Factors from the Injury Patients Include;622
75.1.3;Factors from the Staff of Collecting and Checking Include;622
75.1.4;Factors from the Supervising and Guidance Include;623
75.1.5; Factors from the Process of Inputting Include;623
75.1.6;Factors from the Group of Surveillance Work Include;623
75.1.7;Factors from the Quality Control and Managing Include;623
75.2;Some Basic Concepts[1][2];623
75.3;0.50-0.90 Scale[3];624
75.4;Ranking Based on the Fuzzy Complementary Judgement Matrix[4];625
75.5; Conclusion;628
75.6;References;629
76;Decision-Making Rules Based on Belief Interval with D-S Evidence Theory;630
76.1;Introduction;630
76.2;Decision Rule Based on the Belief Interval;631
76.2.1;The Decision Rule on $Bel$;632
76.2.2;The Decision Rule on $Pl$;632
76.2.3;The Colligation Rule;632
76.2.4;Unitary Operator;633
76.3;Example[5];635
76.4;Conclusion;638
76.5;References;638
77;An Optimization Model for Stimulation of Oilfield at the Stage of High Water Content;639
77.1;Introduction;639
77.2;Source of Data for Professional System ;640
77.3;Method of Optimization for the Adjustment of Oilfield Development;640
77.4;Evaluation of Development Status of Oilfield;640
77.4.1;Method for Predicting the Pressure of Single-Phase Flow;640
77.4.2;Prediction of Residual Oil Saturation;641
77.5;Optimization of Adjustment Project by Fuzzy Theory;641
77.5.1;The First Step;642
77.5.2;The Second Step;642
77.5.3;The Third Step;643
77.6;Method of Integral Optimization;644
77.7;Diagram for the Idea of the Integral Optimization of Adjustment of Water Flooding Oilfield;644
77.8;Applications of the Optimization Model;644
77.8.1;Working out of Development Plan ;644
77.8.2;Application Effect of the Model;645
77.9;Conclusions;646
77.10;References;646
78;New Research of Coefficient-Fuzzy Linear Programming;648
78.1;Introduction;648
78.2;Membership Function of FLP;649
78.3;Solution Method;651
78.3.1;Optimal Solution of FLP;651
78.3.2;Algorithm;652
78.4;Numerical Example;652
78.5;Conclusion;653
78.6;References;653
79;Part VI Artificial Intelligence;655
80;Robust Fuzzy Control for Uncertain Nonlinear Systems with Regional Pole and Variance Constraints;656
80.1;Introduction;656
80.2;Problem Description and Preliminaries;657
80.3;Main Results;661
80.4;Solving Procedures;664
80.5;Conclusions;664
80.6;References;665
81;The Existence of Fuzzy Optimal Control for the Semilinear Fuzzy Integrodifferential Equations with Nonlocal Conditions;666
81.1;Introduction;666
81.2;Preliminaries;667
81.3;Fuzzy Optimal Control;670
81.4;References;674
82;A PSO-Based Approach to Rule Learning in Network Intrusion Detection;675
82.1;Introduction;675
82.2;Standard Particle Swarm Optimization;676
82.3;PSO for Rule Learning;677
82.3.1;Coding Scheme;677
82.3.2;Fitness Function;678
82.3.3;Inertia Weight;678
82.3.4;Algorithm Description;679
82.4;Experiment Results;679
82.5;Conclusions;681
82.6;References;682
83;Similarity Mass and Approximate Reasoning;683
83.1;Introduction;683
83.2;The Construction of $Q$-Formula Mass and $Q$-Logic;684
83.3;Type V Simple Approximate Reasoning Based on $Q$-Logic $C_Q $;684
83.4;Type V Multiple Approximate Reasoning Based on $Q$-Logic $C_Q$;686
83.5;Type V Completeness and Type V Perfection of Knowledge Base K in $Q$-Logic $C_Q$ ;688
83.6;Conclusion ;692
83.7;References;692
84;Intelligent Knowledge Query Answering System Based on Short Message;694
84.1;Introduction;694
84.2;System Framework;695
84.3;Short Message Processing Module;695
84.3.1;Hardware Condition;695
84.3.2;Correlative AT Command and PDU Data Format Analyses;695
84.3.3;Processing Flow of Short Message;697
84.4;Knowledge Query Module;697
84.4.1;Query Mode of Users;697
84.4.2;Knowledge Organization;697
84.4.3;Query Sentences Classification;698
84.4.4;Examples of Parsing Query Sentence;698
84.4.5;Drawing Answer;700
84.5;Conclusion;701
84.6;References;701
85;Comparison Study on Different Core Attributes;702
85.1;Introduction;702
85.2;Preliminary;703
85.3;Core Attributes of Simplified Discernibility Matrix Based on Hu's Discernibility Matrix;704
85.4;Core Attributes of Simplified Discernibility Matrix Based on Positive Region;705
85.5;Core Attributes of Simplified Discernibility Matrix Based on Information Entropy ;706
85.6;Comparison Study on Three Kinds of Core Attributes;710
85.7;Conclusion;711
85.8;References;712
86;Controllability for the Impulsive Semilinear Fuzzy Integrodifferential Equations;713
86.1;Introduction;713
86.2;Existence and Uniqueness of Fuzzy Solution;714
86.3;Controllability;717
86.4;Example;720
86.5;References;722
87;Remodeling for Fuzzy PID Controller Based on Neural Networks;723
87.1;Introduction;723
87.2;Fuzzy PID Controller;724
87.3;Remodeling for an Equivalent NN of Fuzzy PID Controller;728
87.4; Control Simulation;730
87.5;Conclusion;734
87.6;References;734
88;Monitoring for Healthy Sleep Based on Computational Intelligence Information Fusion;735
88.1;Introduction;735
88.2;Scheme of Monitoring Sleep Fidget;736
88.3;Information Fusion Strategy Based on NN;737
88.4;Experimental System for Input/Output Data Acquisition;739
88.5;NN Model of Monitoring Sleep Fidget;741
88.6;Precision Verification of Sleep Fidget Model;745
88.7;Conclusions;747
88.8;References;747
89;Minimization of Mizumoto Automata;748
89.1;Introduction;748
89.2;Mizumoto Automata and Their Equivalent Canonical Form;749
89.3;Minimization of NA;750
89.4;References;751
90;Transformation of Linguistic Truth Values During the Sensor Evaluation;753
90.1;Introduction;753
90.2;Basic Concepts and Hypotheses;754
90.3;Transformation Models;755
90.3.1;Point to Point Model;755
90.3.2;Fuzzy to Point Model;756
90.3.3;Point to Fuzzy Set Model;756
90.3.4;Fuzzy Set to Fuzzy Set Model;756
90.4;Example;757
90.5;Conclusions;758
90.6;References;759
91;Guaranteed Cost Control for a Class of Fuzzy Descriptor Systems with Time-Varying Delay;760
91.1;Introduction;760
91.2;Preliminaries and Problem Formulation;761
91.3;Main Results;762
91.4;Example;767
91.5;Conclusions;767
91.6;References;768
92;The Research and Simulation on the Walking Trajectory of the Hexapod Walking Bio-robot;769
92.1;Foreword;769
92.2;The Brief Introduction About the Integral Structure of the Bionic Hexapod Walking Robot;769
92.3;The Gait Theory Analyses on the Bionic Hexapod Walking Robot;770
92.4;The Trajectory Choices of Bionic Hexapod Walking Robot;771
92.5;The Moving Trajectory Simulation of the Bionic Hexapod Walking Robot;772
92.5.1;The Creation of Bionic Hexapod Walking Robot’s Feet-Tip Trajectory Curve;772
92.5.2;The Virtual Prototype Model Construction of Bionic Hexapod Walking Robot[7];772
92.5.3;Bionic Hexapod Walking Robot’s Kinematic Inverse Solution;774
92.6;End;776
92.7;References;776
93;Research of Group Decision Consensus Degree Based on Extended Intuitionistic Fuzzy Set;777
93.1;Introduction;777
93.2;The Definition of Extended IFS;777
93.3;The Definition of Distance of Extended IFS;778
93.4;Example Analysis;780
93.5;Conclusions;781
93.6;References;781
94;Part VII Rough Sets and Its Application;782
95;A Novel Approach to Roughness Measure in Fuzzy Rough Sets;783
95.1;Introduction;783
95.2;Preliminaries;784
95.3;The Novel Approach to Roughness Measure in Fuzzy Rough Sets;785
95.4;An Example;787
95.5;Conclusions;788
95.6;References;788
96;Rough Communication of Dynamic Concept;789
96.1;Introduction;789
96.2;Two Direction S-Rough Sets and a - Generation of Two Direction Assistant Sets;790
96.3;Rough Communication of Dynamic Concept;791
96.4;Example;792
96.5;Conclusions;793
96.6;References;794
97;Some Entropy for Rough Fuzzy Sets;796
97.1;Introduction;796
97.2;Rough Fuzzy Sets;797
97.2.1; The Expression of Rough Fuzzy Sets;797
97.2.2;The Cardinalities of RFSs;798
97.3;Entropy for RFSs;799
97.4;Conclusions;805
97.5;References;805
98;A Fuzzy Measure Based on Variable Precision Rough Sets;806
98.1;Introduction;806
98.2;Basic Notions Related to Rough Sets;807
98.2.1;Pawlak Rough Sets;807
98.2.2;Information Systems;807
98.2.3;Variable Precision Rough Sets;808
98.3;A Fuzzy Measure Based on VPRS;809
98.3.1;Basic Notions of Fuzzy Sets;809
98.3.2;A Fuzzy Measure Based on VPRS;810
98.4;Conclusion;813
98.5;References;814
99;Rough Sets of System;816
99.1;Introduction;816
99.2;Rough Sets of System;817
99.3;Discussion About the Rough Sets of System;819
99.4;Some Properties of Rough Sets of System;820
99.5;Some Examples of Rough Sets of System;821
99.5.1;Rough Sets on the Real Line;821
99.5.2;Rough Sets of the Additive Group of Integers ;822
99.6;Conclusions ;823
99.7;References;823
100;Synthesis of Concepts Based on Rough Set Theory;824
100.1;Introduction;824
100.2;Rough Sets;825
100.3;Synthesis of Concepts Based on Rough Set Theory;825
100.4;Reduction Based on the Ideas of Synthesis of Concepts;828
100.5;Conclusion and Further Research;831
100.6;References;831
101;.-Tolerance Relation-Based RS Model in IFOIS;833
101.1;Introduction;833
101.2;Basic Theories;834
101.2.1;Incomplete Information System;834
101.2.2;Fuzzy Objective Information System ;835
101.3;.-Tolerance Relation;836
101.4;Rough Set Model in Incomplete and Fuzzy Objective Information System;837
101.4.1;The Concept of Incomplete and Fuzzy Objective Information System [19];837
101.4.2;Rough Set Model;838
101.5;Precision Reduction in Incomplete and Fuzzy Objective Information System;840
101.5.1;Basic Theory of Precision Reduction ;840
101.5.2;Precision Reduction Algorithm;842
101.6;Conclusion;842
101.7;References;843
102;Granular Ranking Algorithm Based on Rough Sets;845
102.1;Introduction;845
102.2;The Granular Ranking Algorithm;846
102.2.1;The Thought of Algorithm Designing;846
102.2.2;The Framework of Algorithm;848
102.2.3;The Description of Granular Ranking Algorithm;848
102.2.4;The Complexity of Algorithm;850
102.3;Experiment;850
102.3.1;Dataset;850
102.3.2;Hit Rate;851
102.3.3;The Result of Experiment;851
102.4;Conclusion;852
102.5;References;853
103;Remote Sensing Image Classification Algorithm Based on Rough Set Theory;854
103.1;Introduction;854
103.2;Principles of Rough Sets;855
103.3;Remote Sensing Image Classification Model Based on Rough Set Theory;856
103.4;Results of Experiment and Analysis;857
103.5;Conclusion;858
103.6;References;859
104;Topological Relations Between Vague Objects in Discrete Space Based on Rough Model;860
104.1;Introduction;860
104.2;Rough Model;861
104.2.1;The Definition of Region and Boundary of Raster Space;861
104.2.2;Definition of Rough Model;863
104.3;Study of Topological Relations Between Vague Objects Based on Rough Model and RCC-D-8;865
104.3.1;The Constraints of Lower and Upper Approximate Regions;865
104.3.2;Study of Topological Relations;867
104.4;Conclusions;867
104.5;References;868
105;Part VIII Application in Fuzzy Mathematics and Systems;870
106;A Least Squares Fuzzy SVM Approach to Credit Risk Assessment;871
106.1;Introduction;871
106.2;Methodology Formulation;872
106.2.1;SVM (By Vapnik [25]);872
106.2.2;FSVM (By Lin and Wang [24]);874
106.2.3;Least Squares FSVM;875
106.3;Experiment Analysis;877
106.4;Conclusions;879
106.5;References;879
107;Similarity Measures on Interval-Valued Fuzzy Sets and Application to Pattern Recognitions;881
107.1;Introduction;881
107.2;Degree of Similarity Between IVFSs and Similarity Measures;882
107.3;Applications of the Similarity Measures to Pattern Recognitions;887
107.4;References;889
108;Updating of Attribute Reduction for the Case of Deleting;890
108.1;Introduction;890
108.2;Preliminaries;891
108.3;Improvement of Discernibility Matrix and Updating of a Core;892
108.4;Updating Principle of Attribute Reduction;894
108.5;Updating Algorithm of Attribute Reduction Based on Discernibility Matrix;895
108.6;Conclusions;898
108.7;References;898
109;The Research of the Exposition Economy Model;900
109.1;Introduction;900
109.2;Investment Multiplier of Exposition;900
109.3;The Value of Development Potential of a City’s Exposition Economy;902
109.4;The Pull Model of Exposition Economy on National Economy;903
109.5;The Theoretical Analysis of the Model;904
109.6;Conclusion;906
109.7;References;907
110;A Region-Based Image Segmentation Method with Kernel FCM;908
110.1;Introduction;908
110.2;Region Segmentation Process;909
110.3;Integrated Feature Extraction;909
110.3.1;Extracting Texture Features;909
110.3.2;Extracting Other Features;911
110.4;Region-Based Color Image Segmentation;911
110.4.1;The Number of the Optimal Clusters;912
110.4.2;Label and Segment Image;913
110.5;Experiment Results;915
110.6;Conclusion and Future Work;916
110.7;References;916
111;An Efficient Threshold Multi-group-Secret Sharing Scheme;917
111.1;Introduction;917
111.2;The Proposed Scheme;918
111.2.1;System Parameters;918
111.2.2;Secret Distribution;918
111.2.3;Secret Reconstruction;920
111.3;Numerical Example;921
111.4;Analyses and Discussions;922
111.4.1;Security Analysis;922
111.4.2;Performance Analysis;923
111.5;Conclusions;924
111.6;References;924
112;A Morphological Approach for Granulometry with Application to Image Denoising;925
112.1;Introduction;925
112.2;Fuzzy Logical Operators;926
112.3;Operations of Fuzzy Sets;926
112.4;Convex Fuzzy Sets;929
112.5;Granulometry;931
112.6;Experiment Results;933
112.7;Conclusion;934
112.8;References;935
113;A Hybrid Decision Tree Model Based on Credibility Theory;936
113.1;Introduction;936
113.2;Credibility Theory;937
113.3;Hybrid Decision Trees Model;938
113.3.1;Fuzzification of Numerical Numbers;938
113.3.2;Hybrid Decision Trees Model;940
113.4;Experimental Results and Discussion;941
113.4.1;Experimental Results;941
113.4.2;Discussion;943
113.5;Conclusion;944
113.6;References;944
114;A Region-Based Image Retrieval Method with Fuzzy Feature;946
114.1;Introduction;946
114.2;Image Segmentation;947
114.3;Region Fuzzy Feature Extraction;948
114.4;Similarities Between Images Computation;950
114.5;Experiment Results and Analysis;951
114.6;Conclusions and Further Work;953
114.7;References;954
115;Association Rule Mining of Kansei Knowledge Using Rough Set;955
115.1;Introduction;955
115.2;Frameworks;956
115.3;Rough Set Theory;956
115.3.1;Review and Background;956
115.3.2;Definition;957
115.3.3;Reduction and Core Computing;958
115.4;Association Rule Mining Based on Rough Set;959
115.4.1;Association Rule Definition;959
115.4.2;Association Rule Algorithm Using Rough Set;960
115.5;Case Study;960
115.5.1;Product Knowledge Representation System Construction;960
115.5.2;Attributes Reduction;961
115.5.3;Strong Rule Extracting;962
115.6;Conclusion and Future Works;963
115.7;References;963
116;A Survey of Fuzzy Decision Tree Classifier Methodology;965
116.1;Introduction;965
116.2;Preliminaries;966
116.3;Potentials and Problems with Fuzzy Decision Tree Classifiers;967
116.4;Special Issues of a Fuzzy Decision Tree Classifier;968
116.4.1;Attribute Selection Criteria in Fuzzy Decision Trees;969
116.4.2;Inference for Decision Assignment;970
116.4.3;Stopping Criteria;971
116.5;Summary and Conclusions;972
116.6;References;973
117;The Optimization for Location for Large Commodity’s Regional Distribution Center;975
117.1;Problem Statement;975
117.2;The Model Hypothesis and Data Processing;976
117.3;The Theory and the Algorithm of Optimization Model for Location;977
117.4;The Calculation Result and Processing;983
117.5;Conclusion;984
117.6;References;985
118;Fuzzy Evaluation of Different Irrigation and Fertilization on Growth of Greenhouse Tomato;986
118.1;Introduction;986
118.2;Materials and Methods;987
118.2.1;Experimental Materials;987
118.2.2;Experimental Design;987
118.2.3;Experimental Method;987
118.2.4;Sampling Collection and Analysis;989
118.3;Result and Analysis;989
118.3.1;Confirm of Model Fuzzy Synthetic Evaluation;989
118.3.2;The Solve of Fuzzy Synthetic Evaluation Model;990
118.3.3; The Result of Fuzzy Synthesis Evaluation Model;991
118.4;Conclusion;992
118.5;References;992
119;The Solution of Linear Programming with LR-Fuzzy Numbers in Objective Function;994
119.1;Introduction;994
119.2;Preliminaries;995
119.3;Fuzzy Linear Programming Problem and Fuzzy Max Order;996
119.4;Possibility and Necessity Maximization Problems;1000
119.5;Numerical Examples;1003
119.6;References;1004
120;On Relationships of Filters in Lattice Implication Algebra;1006
120.1;Introduction;1006
120.2;Preliminaries;1007
120.3;$FL$-Filter of Lattice Implication Algebra;1008
120.4;On FL-Filter and NF-Filter of Lattice Implication Algebra;1010
120.5;Conclusions;1013
120.6;References;1013
121;Study on Adaptive Fuzzy Control System Based on Gradient Descent Learning Algorithm;1015
121.1;Introduction;1015
121.2;How to Design a General Fuzzy Control System;1016
121.3;Adaptive Fuzzy Control Algorithm;1017
121.3.1; System Structure;1017
121.3.2; Adaptive Control Algorithm;1017
121.4;Fuzzy Control Rules;1019
121.5;Simulation Results and Discussions;1021
121.5.1;Step Response;1021
121.5.2; Ramp Response;1023
121.5.3; Acceleration Response;1023
121.5.4; Sine Response;1023
121.5.5; Discussions;1024
121.6;Stability Discussion;1025
121.7;Conclusions;1025
121.8;References;1026
122;Uncertainty Measure of Fuzzy Rough Set;1027
122.1;Introduction;1027
122.2;Prelimilary;1027
122.3;Rough Entropy of Fuzzy Rough Set;1029
122.3.1;Information Entropy and Rough Entropy of Fuzzy Knowledge R;1029
122.3.2;Rough Entropy of Fuzzy Rough Set;1030
122.4;Conclusions;1032
122.5;References;1032
123;Author Index;1034