1  20
Next
 Beverly, MA : Scrivener Publishing ; Hoboken, NJ : Wiley, 2021.
 Description
 Book — 1 online resource (431 pages)
 Summary

 Preface xiii 1 Fuzzy Fractals in Cervical Cancer 1 T. Sudha and G. Jayalalitha 1.1 Introduction 2 1.1.1 Fuzzy Mathematics 2 1.1.1.1 Fuzzy Set 2 1.1.1.2 Fuzzy Logic 2 1.1.1.3 Fuzzy Matrix 3 1.1.2 Fractals 3 1.1.2.1 Fractal Geometry 4 1.1.3 Fuzzy Fractals 4 1.1.4 Cervical Cancer 5 1.2 Methods 7 1.2.1 Fuzzy Method 7 1.2.2 Sausage Method 11 1.3 Maximum Modulus Theorem 15 1.4 Results 18 1.4.1 Fuzzy Method 19 1.4.2 Sausage Method 20 1.5 Conclusion 21 References 23 2 Emotion Detection in IoTBased ELearning Using Convolution Neural Network 27 Latha Parthiban and S. Selvakumara Samy 2.1 Introduction 28 2.2 Related Works 30 2.3 Proposed Methodology 31 2.3.1 Students Emotion Recognition Towards the Class 31 2.3.2 Eye GazeBased Student Engagement Recognition 31 2.3.3 Facial Head MovementBased Student Engagement Recognition 34 2.4 Experimental Results 35 2.4.1 Convolutional Layer 35 2.4.2 ReLU Layer 35 2.4.3 Pooling Layer 36 2.4.4 Fully Connected Layer 36 2.5 Conclusions 42 References 42 3 Fuzzy Quotient3 Cordial Labeling of Some Trees of Diameter 5Part III 45 P. Sumathi and J. Suresh Kumar 3.1 Introduction 46 3.2 Related Work 46 3.3 Definition 47 3.4 Notations 47 3.5 Main Results 48 3.6 Conclusion 71 References 71 4 Classifying Fuzzy MultiCriterion Decision Making and Evolutionary Algorithm 73 Kirti Seth and Ashish Seth 4.1 Introduction 74 4.1.1 Classical Optimization Techniques 74 4.1.2 The BioInspired Techniques Centered on Optimization 75 4.1.2.1 Swarm Intelligence 77 4.1.2.2 The Optimization on Ant Colony 78 4.1.2.3 Particle Swarm Optimization (PSO) 82 4.1.2.4 Summary of PSO 83 4.2 Multiple Criteria That is Used for Decision Making (MCDM) 83 4.2.1 WSM Method 86 4.2.2 WPM Method 86 4.2.3 Analytic Hierarchy Process (AHP) 87 4.2.4 TOPSIS 89 4.2.5 VIKOR 90 4.3 Conclusion 91 References 91 5 Fuzzy TriMagic Labeling of Isomorphic Caterpillar Graph J62,3,4 of Diameter 5 93 P. Sumathi and C. Monigeetha 5.1 Introduction 93 5.2 Main Result 95 5.3 Conclusion 154 References 154 6 Fuzzy TriMagic Labeling of Isomorphic Caterpillar Graph J6 2,3,5 of Diameter 5 155 P. Sumathi and C. Monigeetha 6.1 Introduction 155 6.2 Main Result 157 6.3 Conclusion 215 References 215 7 Ceaseless RuleBased Learning Methodology for Genetic Fuzzy RuleBased Systems 217 B. Siva Kumar Reddy, R. Balakrishna and R. Anandan 7.1 Introduction 218 7.1.1 Integration of Evolutionary Algorithms and Fuzzy Logic 219 7.1.2 Fuzzy LogicAided Evolutionary Algorithm 220 7.1.3 Adaptive Genetic Algorithm That Adapt Manage Criteria 220 7.1.4 Genetic Algorithm With Fuzzified Genetic Operators 220 7.1.5 Genetic Fuzzy Systems 220 7.1.6 Genetic Learning Process 223 7.2 Existing Technology and its Review 223 7.2.1 Techniques for RuleBased Understanding with Genetic Algorithm 223 7.2.2 Strategy A: GA Primarily Based Optimization for Computerized Built FLC 223 7.2.3 Strategy B: GABased Optimization of Manually Created FLC 224 7.2.4 Methods of Hybridization for GFS 225 7.2.4.1 The Michigan StrategyClassifier System 226 7.2.4.2 The Pittsburgh Method 229 7.3 Research Design 233 7.3.1 The Ceaseless Rule Learning Approach (CRL) 233 7.3.2 Multistage Processes of Ceaseless Rule Learning 234 7.3.3 Other Approaches of Genetic Rule Learning 236 7.4 Findings or Result Discussion so for in the Area of GFS Hybridization 237 7.5 Conclusion 239 References 240 8 Using Fuzzy Technique Management of Configuration and Status of VM for Task Distribution in Cloud System 243 Yogesh Shukla, Pankaj Kumar Mishra and Ramakant Bhardwaj 8.1 Introduction 244 8.2 Literature Review 244 8.3 Logic System for Fuzzy 246 8.4 Proposed Algorithm 248 8.4.1 Architecture of System 248 8.4.2 Terminology of Model 250 8.4.3 Algorithm Proposed 252 8.4.4 Explanations of Proposed Algorithm 254 8.5 Results of Simulation 257 8.5.1 Cloud System Numerical Model 257 8.5.2 Evaluation Terms Definition 258 8.5.3 Environment Configurations Simulation 259 8.5.4 Outcomes of Simulation 259 8.6 Conclusion 260 References 266 9 Theorems on Fuzzy Soft Metric Spaces 269 Qazi Aftab Kabir, Ramakant Bhardwaj and Ritu Shrivastava 9.1 Introduction 269 9.2 Preliminaries 270 9.3 FSMS 271 9.4 Main Results 273 9.5 Fuzzy Soft Contractive Type Mappings and Admissible Mappings 278 References 282 10 Synchronization of TimeDelay Chaotic System with Uncertainties in Terms of TakagiSugeno Fuzzy System 285 Sathish Kumar Kumaravel, Suresh Rasappan and Kala Raja Mohan 10.1 Introduction 285 10.2 Statement of the Problem and Notions 286 10.3 Main Result 291 10.4 Numerical Illustration 302 10.5 Conclusion 312 References 312 11 Trapezoidal Fuzzy Numbers (TrFN) and its Application in Solving Assignment Problem by Hungarian Method: A New Approach 315 Rahul Kar, A.K. Shaw and J. Mishra 11.1 Introduction 316 11.2 Preliminary 317 11.2.1 Definition 317 11.2.2 Some Arithmetic Operations of Trapezoidal Fuzzy Number 318 11.3 Theoretical
 Part 319 11.3.1 Mathematical Formulation of an Assignment Problem 319 11.3.2 Method for Solving an Assignment Problem 320 11.3.2.1 Enumeration Method 320 11.3.2.2 Regular Simplex Method 321 11.3.2.3 Transportation Method 321 11.3.2.4 Hungarian Method 321 11.3.3 Computational Processor of Hungarian Method (For Minimization Problem) 323 11.4 Application With Discussion 325 11.5 Conclusion and Further Work 331 References 332 12 The Connectedness of Fuzzy Graph and the Resolving Number of Fuzzy Digraph 335 Mary Jiny D. and R. Shanmugapriya 12.1 Introduction 336 12.2 Definitions 336 12.3 An Algorithm to Find the Super Resolving Matrix 341 12.3.1 An Application on Resolving Matrix 344 12.3.2 An Algorithm to Find the Fuzzy Connectedness Matrix 347 12.4 An Application of the Connectedness of the Modified Fuzzy Graph in Rescuing Human Life From Fire Accident 349 12.4.1 Algorithm to Find the Safest and Shortest Path Between Two Landmarks 352 12.5 Resolving Number Fuzzy Graph and Fuzzy Digraph 356 12.5.1 An Algorithm to Find the Resolving Set of a Fuzzy Digraph 360 12.6 Conclusion 362 References 362 13 A Note on Fuzzy Edge Magic Total Labeling Graphs 365 R. Shanmugapriya and P.K. Hemalatha 13.1 Introduction 365 13.2 Preliminaries 366 13.3 Theorem 367 13.3.1 Example 368 13.4 Theorem 370 13.4.1 Example 371 13.4.1.1 Lemma 374 13.4.1.2 Lemma 374 13.4.1.3 Lemma 374 13.5 Theorem 374 13.5.1 Example as Shown in Figure 13.5 Star Graph S(1,9) is FEMT Labeling 374 13.6 Theorem 376 13.7 Theorem 377 13.7.1 Example 378 13.8 Theorem 380 13.9 Theorem 381 13.10 Application of Fuzzy Edge Magic Total Labeling 383 13.11 Conclusion 385 References 385 14 The Synchronization of Impulsive TimeDelay Chaotic Systems with Uncertainties in Terms of TakagiSugeno Fuzzy System 387 Balaji Dharmalingam, Suresh Rasappan, V. Vijayalakshmi and G. Suseendran 14.1 Introduction 387 14.2 Problem Description and Preliminaries 389 14.2.1 Impulsive Differential Equations 389 14.3 The TS Fuzzy Model 391 14.4 Designing of Fuzzy Impulsive Controllers 393 14.5 Main Result 394 14.6 Numerical Example 400 14.7 Conclusion 410 References 410 15 Theorems on Soft Fuzzy Metric Spaces by Using Control Function 413 Sneha A. Khandait, Chitra Singh, Ramakant Bhardwaj and Amit Kumar Mishra 15.1 Introduction 413 15.2 Preliminaries and Definition 414 15.3 Main Results 415 15.4 Conclusion 429 References 429 16 On Soft ( , )Continuous Functions in Soft Topological Spaces 431 N. Kalaivani, E. Chandrasekaran and K. Fayaz Ur Rahman 16.1 Introduction 432 16.2 Preliminaries 432 16.2.1 Outline 432 16.2.2 Soft Open Set 432 16.2.3 Soft Ti Spaces 434 16.2.4 Soft ( , s)Continuous Functions 436 16.3 Soft ( , )Continuous Functions in Soft Topological Spaces 438 16.3.1 Outline 438 16.3.2 Soft ( , )Continuous Functions 438 16.3.3 Soft ( , )Open Functions 444 16.3.4 Soft ( , )Closed Functions 447 16.3.5 Soft ( , )Homeomorphism 450 16.3.6 Soft ( , s)Contra Continuous Functions 450 16.3.7 Soft ( , )Contra Continuous Functions 455 16.4 Conclusion 459 References 459 Index 461.
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(source: Nielsen Book Data)
2. Introduction to fuzzy systems [2006]
 Chen, G. (Guanrong)
 Boca Raton, FL : Chapman & Hall/CRC, c2006.
 Description
 Book — xiii, 315 p. : ill. ; 25 cm.
 Summary

 Each chapters ends with a problem set Fuzzy Set Theory: Classical Set Theory Fuzzy Set Theory Interval Arithmetic Operations on Fuzzy Sets: Fuzzy Logic Theory Classical Logic Theory The Boolean Algebra MultiValued Logic Fuzzy Logic and Approximate Reasoning Fuzzy Relations Some Applications of Fuzzy Logic Product Quality Evaluation Decision Making for Investment Performance Evaluation Miscellaneous Examples Fuzzy Rule Base and Fuzzy Modeling Fuzzy Rule Base Fuzzy Modeling Fuzzy Control Systems Classical Programmable Logic Control Fuzzy Logic Control: A General ModelFree Approach Fuzzy PID Control Systems Conventional PID Controllers Fuzzy PID Controllers (Type 1) Fuzzy PID Controllers (Type 2) Fuzzy PID Controllers: Stability Analysis Computational Verb Fuzzy Controllers Computational Verbs and Verb Numbers Verb Rules and Verb Inference Computational VerbBased Fuzzy PID Controllers References Solutions Index...
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Stacks  Request (opens in new tab) 
QA402 .C445 2006  Available 
 Ramer, Arthur.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1992]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192949  Unknown 
4. Strongly transitive fuzzy relations [microform] : a more adequate way to describe similarity [1992]
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1992]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192951  Unknown 
5. Toward a theory of fuzzy systems [1969]
 Zadeh, Lotfi A. (Lotfi Asker)
 [Washington, National Aeronautics and Space Administration]; for sale by the Clearinghouse for Federal Scientific and Technical Information, Springfield, Va. [1969]
 Description
 Book — v, 36 p. 27 cm.
 Online
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  
NASA CR 1432  Unknown 
 Iranian Conference on Fuzzy Systems (13th : 2013 : Qazvīn, Iran)
 Piscataway, NJ : IEEE, c2013.
 Description
 Book — 1 online resource : ill. (some col.).
 Gegov, Alexander.
 Berlin ; Heidelberg : Springer, ©2011.
 Description
 Book — 1 online resource (xi, 290 pages)
 Summary

 Introduction
 Types of Fuzzy Systems
 Formal Models for Fuzzy Networks
 Basic Operations in Fuzzy Networks
 Structural Properties of Basic Operations
 Advanced Operations in Fuzzy Networks
 Feedforward Fuzzy Networks
 Feedback Fuzzy Networks
 Evaluation of Fuzzy Networks
 Conclusion.
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1993]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192950  Unknown 
 Niskanen, Vesa A.
 Helsinki : Dept. of Education, University of Helsinki, 1990.
 Description
 Book — 16 p. ; 21 cm.
 Online
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Stacks  
L48 .H4 NO.76  Unknown 
 North American Fuzzy Information Processing Society. Annual Meeting (2020 : Redmond, Wash.)
 Cham : Springer, [2022]
 Description
 Book — 1 online resource : illustrations (chiefly color) Digital: text file.PDF.
 Summary

 Powerset operators in categories with fuzzy relations dened by monads. Improved Fuzzy QLearning with Replay Memory. The ulem package: underlining for emphasis. A Dynamic Hierarchical GeneticFuzzy Sugeno Network. Fuzzy Mathematical Morphology and Applications in Image Processing.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Singapore : Springer, 2016.
 Description
 Book — 1 online resource (xix, 257 pages) : illustrations (some color) Digital: text file.PDF.
 Summary

 Introduction. Stability and Stabilization of CTIT2FMBSs. Outputfeedback Control of CTIT2FMBSs. SampledData Control of CTIT2FMBSs. Output Tracking Control of CTIT2FSs. Switched Outputfeedback Control of CTIT2FMBSs. Filter Design of CTIT2FMBSs. Fault Detection of CTIT2FMBSs. Model Reduction of CTIT2FMBSs. Optimal Control of DTIT2FMBSs. Statefeedback Control of DTIT2FMBSs. Static Outputfeedback Control of DTIT2FMBSs. Guaranteed Cost Output Tracking Control of DTIT2FMBSs.
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 Benzaouia, Abdellah, author.
 Cham : Springer, 2014.
 Description
 Book — 1 online resource (xxxii, 294 pages) : illustrations (some color) Digital: text file.PDF.
 Summary

 Introduction to TakagiSugeno Fuzzy Systems
 Stabilization of TakagiSugeno Fuzzy Systems with Constrained Controls
 Static Output Feedback Control for Fuzzy Systems
 Stabilization of Discretetime TakagiSugeno Fuzzy Positive Systems
 Stabilization of Delayed TS Fuzzy Positive Systems
 Robust Control of TS Fuzzy Systems with Timevarying Delay
 Robust Output H[infinity] Fuzzy Control.Stabilization of Discretetime TS Fuzzy Positive Systems with Multiple Delays
 Stabilization of Two Dimensional TS Fuzzy Systems.
13. Modern adaptive fuzzy control systems [2023]
 Mohammadzadeh, Ardashir.
 Cham : Springer, [2023]
 Description
 Book — 1 online resource (161 p.).
 Summary

 Chapter 1: An Introduction to Fuzzy and Fuzzy Control Systems.
 Chapter 2: Classification of Adaptive Fuzzy Controllers.
 Chapter 3: Type2 Fuzzy Systems.
 Chapter 4: Training Interval Type2 Fuzzy Systems Based on Error Backpropagation.
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 Alonso Moral, Jose Maria, author.
 Cham : Springer, [2021]
 Description
 Book — 1 online resource
 Summary

 Toward Explainable Artificial Intelligence through Fuzzy Systems. An Overview of Fuzzy Systems. Interpretability Constraints and Criteria for Fuzzy Systems. Revisiting Indexes for Assessing Interpretability of Fuzzy Systems. Designing Interpretable Fuzzy Systems.
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15. Fuzzy pictures as philosophical problem and scientific practice : a study of visual vagueness [2017]
 Cat, Jordi, author.
 Cham, Switzerland : Springer, [2017]
 Description
 Book — 1 online resource (xiv, 194 pages : illustrations) Digital: text file; PDF.
 Summary

 1. Introduction: visual uncertainty, categorization, objectivity and practices andvalues of imprecision. 2. From ordinary to mathematical categorization in the visual world... of words, pictures and practices. 3. Vagueness and fuzziness in words and predication. 4. Representations: from words to images. 5. Epistemology, aesthetics and pragmatics of scientific and other images:visualization, representation and reasoning. 6. Visual representation: from perceptions to pictures. 7. Vague pictures: epistemology, aesthetics and pragmatics of fuzziness, from fuzzy perception to fuzzy pictures. 8. Blur as vagueness: seeing images vaguely and seeing vague images
 perception and representation. 9. Vague pictures as pictures. 10. Vague pictures as vague representations and representing. 11. The cognitive values of imprecision: towards a scientific epistemology, aesthetics and pragmatics of fuzziness, contextual lessons from the history of picturemaking practices. 12. Introduction: fuzzyset representation and processing of fuzzy images
 nonlinguistic vagueness as scientific practice
 scientific epistemology, aesthetics, methodology and technology of fuzziness. 13. Application of mathematics in the representation of images: from geometry to set theory. 14. Cognitive framework of settheoretic methodology of analysis and synthesis: categorization, classification and many faces of digital geometry. 15. Conceptual resources and philosophical grounds in settheoretic models of vagueness: fuzzy, rough and near sets. 16. Analytic and synthetic forms of vague categorization. 17. From intrinsic to extrinsic vague categorization and content. 18. Pictorial representation and simplicity of categorization. 19. Fuzzy visual thinking: interpreting and thinking with fuzzy pictures and fuzzydata. 20. Pictorial approximation: pictorial accuracy, vagueness and fuzziness. 21. Pictorial vagueness as scientific practice: picturemaking and the mathematical practice of fuzzy categorization. 22. Conclusion.
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16. Fuzzy measurement of sustainability [2009]
 Phillis, Yannis A., 1950
 New York : Nova Science Publishers, c2009.
 Description
 Book — viii, 183 p. : ill. ; 27 cm.
 Summary

 Introduction
 Introduction to Fuzzy Logic
 Sustainability Indicators
 Fuzzy Assessment
 Sustainability of Organisations
 Index.
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 Online
17. Fuzzy implications [electronic resource] [2008]
 Baczy℗þnski, Micha{lstrok}.
 Berlin ; New York : Springer, 2008.
 Description
 Book — xviii, 310 p. : ill., graph.
 Chichester, England ; Hoboken, NJ : John Wiley & Sons, c2007.
 Description
 Book — xx, 434 p. : ill. ; 26 cm.
 Summary

 List of Contributors.Foreword.Preface.Part I Fundamentals.1 Fundamentals of Fuzzy Clustering (Rudolf Kruse, Christian Doring and MarieJeanne Lesot).1.1 Introduction.1.2 Basic Clustering Algorithms.1.3 Distance Function Variants.1.4 Objective Function Variants.1.5 Update Equation Variants: Alternating Cluster Estimation.1.6 Concluding Remarks.Acknowledgements.References.2 Relational Fuzzy Clustering (Thomas A. Runkler).2.1 Introduction.2.2 Object and Relational Data.2.3 Object Data Clustering Models.2.4 Relational Clustering.2.5 Relational Clustering with Nonspherical Prototypes.2.6 Relational Data Interpreted as Object Data.2.7 Summary.2.8 Experiments.2.9 Conclusions.References.3 Fuzzy Clustering with Minkowski Distance Functions (Patrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen).3.1 Introduction.3.2 Formalization.3.3 The Majorizing Algorithm for Fuzzy Cmeans with Minkowski Distances.3.4 The Effects of the Robustness Parameter.3.5 Internet Attitudes.3.6 Conclusions.References.4 Soft Cluster Ensembles (Kunal Punera and Joydeep Ghosh).4.1 Introduction.4.2 Cluster Ensembles.4.3 Soft Cluster Ensembles.4.4 Experimental Setup.4.5 Soft vs. Hard Cluster Ensembles.4.6 Conclusions and Future Work.Acknowledgements.References.Part II Visualization.5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures (Janos Abonyi and Balazs Feil).5.1 Problem Definition.5.2 Classical Methods for Cluster Validity and Merging.5.3 Similarity of Fuzzy Clusters.5.4 Visualization of Clustering Results.5.5 Conclusions.Appendix 5A.1 Validity Indices.Appendix 5A.2 The Modified Sammon Mapping Algorithm.Acknowledgements.References.6 Interactive Exploration of Fuzzy Clusters (Bernd Wiswedel, David E. Patterson and Michael R. Berthold).6.1 Introduction.6.2 Neighborgram Clustering.6.3 Interactive Exploration.6.4 Parallel Universes.6.5 Discussion.References.Part III Algorithms and Computational Aspects.7 Fuzzy Clustering with Participatory Learning and Applications (Leila Roling Scariot da Silva, Fernando Gomide and Ronald Yager).7.1 Introduction.7.2 Participatory Learning.7.3 Participatory Learning in Fuzzy Clustering.7.4 Experimental Results.7.5 Applications.7.6 Conclusions.Acknowledgements.References.8 Fuzzy Clustering of Fuzzy Data (Pierpaolo D'Urso).8.1 Introduction.8.2 Informational Paradigm, Fuzziness and Complexity in Clustering Processes.8.3 Fuzzy Data.8.4 Fuzzy Clustering of Fuzzy Data.8.5 An Extension: Fuzzy Clustering Models for Fuzzy Data Time Arrays.8.6 Applicative Examples.8.7 Concluding Remarks and Future Perspectives.References.9 Inclusionbased Fuzzy Clustering (Samia NeftiMeziani and Mourad Oussalah).9.1 Introduction.9.2 Background: Fuzzy Clustering.9.3 Construction of an Inclusion Index.9.4 Inclusionbased Fuzzy Clustering.9.5 Numerical Examples and Illustrations.9.6 Conclusions.Acknowledgements.Appendix 9A.1.References.10 Mining Diagnostic Rules Using Fuzzy Clustering (Giovanna Castellano, Anna M. Fanelli and Corrado Mencar).10.1 Introduction.10.2 Fuzzy Medical Diagnosis.10.3 Interpretability in Fuzzy Medical Diagnosis.10.4 A Framework for Mining Interpretable Diagnostic Rules.10.5 An Illustrative Example.10.6 Concluding Remarks.References.11 Fuzzy Regression Clustering (Mikal SatoIlic).11.1 Introduction.11.2 Statistical Weighted Regression Models.11.3 Fuzzy Regression Clustering Models.11.4 Analyses of Residuals on Fuzzy Regression Clustering Models.11.5 Numerical Examples.11.6 Conclusion.References.12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling Using the Weighted Fuzzy Cmeans (George E. Tsekouras).12.1 Introduction.12.2 Takagi and Sugeno's Fuzzy Model.12.3 Hierarchical Clusteringbased Fuzzy Modeling.12.4 Simulation Studies.12.5 Conclusions.References.13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data (Mario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni).13.1 Introduction.13.2 Dissimilarity Modeling.13.3 Relational Clustering.13.4 Experimental Results.13.5 Conclusions.References.14 Simultaneous Clustering and Feature Discrimination with Applications (Hichem Frigui).14.1 Introduction.14.2 Background.14.3 Simultaneous Clustering and Attribute Discrimination (SCAD).14.4 Clustering and Subset Feature Weighting.14.5 Case of Unknown Number of Clusters.14.6 Application
 1: Color Image Segmentation.14.7 Application
 2: Text Document Categorization and Annotation.14.8 Application
 3: Building a Multimodal Thesaurus from Annotated Images.14.9 Conclusions.Appendix 14A.1.Acknowledgements.References.Part IV Realtime and Dynamic Clustering.15 Fuzzy Clustering in Dynamic Data Mining  Techniques and Applications (Richard Weber).15.1 Introduction.15.2 Review of Literature Related to Dynamic Clustering.15.3 Recent Approaches for Dynamic Fuzzy Clustering.15.4 Applications.15.5 Future Perspectives and Conclusions.Acknowledgement.References.16 Fuzzy Clustering of Parallel Data Streams (Jurgen Beringer and Eyke Hullermeier).16.1 Introduction.16.2 Background.16.3 Preprocessing and Maintaining Data Streams.16.4 Fuzzy Clustering of Data Streams.16.5 Quality Measures.16.6 Experimental Validation.16.7 Conclusions.References.17 Algorithms for Realtime Clustering and Generation of Rules from Data (Dimitar Filev and Plamer Angelov).17.1 Introduction.17.2 Densitybased Realtime Clustering.17.3 FSPC: Realtime Learning of Simplified Mamdani Models.17.4 Applications.17.5 Conclusion.References.Part V Applications and Case Studies.18 Robust Exploratory Analysis of Magnetic Resonance Images using FCM with Feature Partitions (Mark D. Alexiuk and Nick J. Pizzi).18.1 Introduction.18.2 FCM with Feature Partitions.18.3 Magnetic Resonance Imaging.18.4 FMRI Analysis with FCMP.18.5 Datasets.18.6 Results and Discussion.18.7 Conclusion.Acknowledgements.References.19 Concept Induction via Fuzzy Cmeans Clustering in a Highdimensional Semantic Space (Dawei Song, Guihong Cao, Peter Bruza and Raymond Lau).19.1 Introduction.19.2 Constructing a Highdimensional Semantic Space via Hyperspace Analogue to Language.19.3 Fuzzy Cmeans Clustering.19.4 Word Clustering on a HAL Space  A Case Study.19.5 Conclusions and Future Work.Acknowledgement.References.20 Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells using FTIR Spectroscopy (XiaoYing Wang, Jonathan M. Garibaldi, Benjamin Bird and Mike W. George).20.1 Introduction.20.2 Clustering Techniques.20.3 Cluster Validity.20.4 Simulated Annealing Fuzzy Clustering Algorithm.20.5 Automatic Cluster Merging Method.20.6 Conclusion.Acknowledgements.References.Index.
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Stacks  Request (opens in new tab) 
QA248.5 .A35 2007  Available 
19. Complexity management in fuzzy systems [electronic resource] : a rule base compression approach [2007]
 Gegov, Alexander.
 Berlin ; London : Springer, c2007.
 Description
 Book — xiii, 351 p. : ill. (some col.).
 Summary

 Basic Types of Fuzzy Rule Based Systems. Rule Base Reduction Methods for Fuzzy Systems. Formal Presentation of Fuzzy Rule Based Systems. Formal Manipulation of Fuzzy Rule Based Systems. Formal Manipulation with Special Rule Bases. Formal Transformation of Fuzzy Rule Based Systems. Formal Transformation of Feedback Rule Bases. Formal Simplification of Fuzzy Rule Based Systems. Conclusion.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Pedrycz, Witold, 1953
 Hoboken, N.J. : WileyInterscience : IEEE Press, c2007.
 Description
 Book — xxii, 526 p. : ill. ; 25 cm.
 Summary

 Preface.1 Introduction.1.1 Digital communities and a fundamental quest for humancentric systems.1.2 A historical overview: towards a nonAristotelian perspective of the world.1.3 Granular Computing.1.4 Quantifying information granularity: generality versus specificity.1.5 Computational Intelligence.1.6 Granular Computing and Computational Intelligence.1.7 Conclusions.Exercises and problems.Historical notes.References.2 Notions and Concepts of Fuzzy Sets.2.1 Sets and fuzzy sets: a departure from the principle of dichotomy.2.2 Interpretation of fuzzy sets.2.3 Membership functions and their motivation.2.4 Fuzzy numbers and intervals.2.5 Linguistic variables.2.6 Conclusions.Exercises and problems.Historical notes.References.3 Characterization of Fuzzy Sets.3.1 A generic characterization of fuzzy sets: some fundamental descriptors.3.2 Equality and inclusion relationships in fuzzy sets.3.3 Energy and entropy measures of fuzziness.3.4 Specificity of fuzzy sets.3.5 Geometric interpretation of sets and fuzzy sets.3.6 Granulation of information.3.7 Characterization of the families of fuzzy sets.3.8 Fuzzy sets, sets, and the representation theorem.3.9 Conclusions.Exercises and problems.Historical notes.References.4 The Design of Fuzzy Sets.4.1 Semantics of fuzzy sets: some general observations.4.2 Fuzzy set as a descriptor of feasible solutions.4.3 Fuzzy set as a descriptor of the notion of typicality.4.4 Membership functions in the visualization of preferences of solutions.4.5 Nonlinear transformation of fuzzy sets.4.6 Vertical and horizontal schemes of membership estimation.4.7 Saaty's priority method of pairwise membership function estimation.4.8 Fuzzy sets as granular representatives of numeric data.4.9 From numeric data to fuzzy sets.4.10 Fuzzy equalization.4.11 Linguistic approximation.4.12 Design guidelines for the construction of fuzzy sets.4.13 Conclusions.Exercises and problems.Historical notes.References.5 Operations and Aggregations of Fuzzy Sets.5.1 Standard operations on sets and fuzzy sets.5.2 Generic requirements for operations on fuzzy sets.5.3 Triangular norms.5.4 Triangular conorms.5.5 Triangular norms as a general category of logical operators.5.6 Aggregation operations.5.7 Fuzzy measure and integral.5.8 Negations.5.9 Conclusions.Exercises and problems.Historical notes.References.6 Fuzzy Relations.6.1 The concept of relations.6.2 Fuzzy relations.6.3 Properties of the fuzzy relations.6.4 Operations on fuzzy relations.6.5 Cartesian product, projections and cylindrical extension of fuzzy sets.6.6 Reconstruction of fuzzy relations.6.7 Binary fuzzy relations.6.8 Conclusions.Exercises and problems.Historical notes.References.7 Transformations of Fuzzy Sets.7.1 The extension principle.7.2 Compositions of fuzzy relations.7.3 Fuzzy relational equations.7.4 Associative Memories.7.5 Fuzzy numbers and fuzzy arithmetic.7.6 Conclusions.Exercises and problems.Historical notes.References.8 Generalizations and Extensions of Fuzzy Sets.8.1 Fuzzy sets of higher order.8.2 Rough fuzzy sets and fuzzy rough sets.8.3 Intervalvalued fuzzy sets.8.4 Type2 fuzzy sets.8.5 Shadowed sets as a threevalued logic characterization of fuzzy sets.8.6 Probability and fuzzy sets.8.7 Probability of fuzzy events.8.8 Conclusions.Exercises and problems.Historical notes.References.9 Interoperability Aspects of Fuzzy Sets.9.1 Fuzzy set and its family of scuts.9.2 Fuzzy sets and their interfacing with the external world.9.3 Encoding and decoding as an optimization problem of vector quantization.9.4 Decoding of a fuzzy set through a family of fuzzy sets.9.5 Taxonomy of data in structure description with shadowed sets.9.6 Conclusions.Exercises and problems.Historical notes.References
 .10. Fuzzy Modeling: Principles and Methodology.10.1 The architectural blueprint of fuzzy models.10.2 Key phases of the development and use of fuzzy models.10.3 Main categories of fuzzy models: an overview.10.4 Verification and validation of fuzzy models.10.5 Conclusions.Exercises and problems.Historical notes.References.11 Rulebased Fuzzy Models.11.1 Fuzzy rules as a vehicle of knowledge representation.11.2 General categories of fuzzy rules and their semantics.11.3 Syntax of fuzzy rules.11.4 Basic Functional Modules: Rule base, Database, and Inference scheme.11.5 Types of RuleBased Systems and Architectures.11.6 Approximation properties of fuzzy rulebased models.11.7 Development of RuleBased Systems.11.8 Parameter estimation procedure for functional rulebased systems.11.9 Design issues of rulebased systems  consistency, completeness, and the curse of dimensionality.11.10 The curse of dimensionality in rulebased systems.11.11 Development scheme of fuzzy rulebased models.11.12 Conclusions.Exercises and problems.Historical notes.References.12 From Logic Expressions to Fuzzy Logic Networks.12.1 Introduction.12.2 Main categories of fuzzy neurons.12.3 Uninormbased fuzzy neurons.12.4 Architectures of logic networks.12.5 The development mechanisms of the fuzzy neural networks.12.6 Interpretation of the fuzzy neural networks.12.7 From fuzzy logic networks to Boolean functions and their minimization through learning.12.8 Interfacing the fuzzy neural network.12.9 Interpretation aspects  a refinement of induced rulebased system.12.10 Reconciliation of perception of information granules and granular mappings.12.11 Conclusions.Exercises and problems.Historical notes.References
 .13. Fuzzy Systems and Computational Intelligence.13.1 Computational Intelligence.13.2 Recurrent neurofuzzy systems.13.3 Genetic fuzzy systems.13.4 Coevolutionary hierarchical genetic fuzzy system.13.5 Hierarchical collaborative relations.13.6 Evolving fuzzy systems.13.7 Conclusions.Exercises and problems.Historical notes.References
 .14. Granular Models and Human Centric Computing.14.1 The clusterbased representation of the input  output mappings.14.2 Contextbased clustering in the development of granular models.14.3 Granular neuron as a generic processing element in granular networks.14.4 Architecture of granular models based on conditional fuzzy clustering.14.5 Refinements of granular models.14.6 Incremental granular models.14.7 Humancentric fuzzy clustering.14.8 Participatory learning in fuzzy clustering.14.9 Conclusions.Exercises and problems.Historical notes.References
 .15. Emerging Trends in Fuzzy Systems.15.1 Relational ontology in information retrieval.15.2 Multiagent fuzzy systems.15.3 Distributed fuzzy control.15.4 Conclusions.Exercises and problems.Historical notes.References.Appendix A: Mathematical Prerequisites.Appendix B: Neurocomputing.Appendix C: Biologically Inspired Optimization.Index.
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