Fuzzy set and its extension : the intuitionistic fuzzy set / by Chaira, Tamalika

Table of Contents: “…Preface xiii -- Organization of the Book xv -- 1 Fuzzy/Intuitionistic Fuzzy Set Theory 1 -- 1.1 Introduction to Fuzzy Set 1 -- 1.2 Mathematical Representation of Fuzzy Sets 3 -- 1.3 Membership Function 6 -- 1.4 Fuzzy Relations 10 -- 1.5 Projection 13 -- 1.6 Composition of Fuzzy Relation 14 -- 1.7 Fuzzy Binary Relation 19 -- 1.8 Transitive Closure of Fuzzy Binary Relation 21 -- 1.9 Fuzzy Equivalence Relation 23 -- 1.10 Intuitionistic Fuzzy Set 24 -- 1.11 Construction of Intuitionistic Fuzzy Set 26 -- 1.12 Intuitionistic Fuzzy Relations 29 -- 1.13 Composition of Intuitionistic Fuzzy Relation 31 -- 1.13.1 Composition of IFR Using T-norms and T-conorms 32 -- 1.14 Intuitionistic Fuzzy Binary Relation 34 -- 1.14.1 Reflexive Property 34 -- 1.14.2 Symmetric Property 37 -- 1.14.3 Transitive Property 38 -- 1.15 Summary 39 -- References 39 -- 2 Playing with Fuzzy/Intuitionistic Fuzzy Numbers 41 -- 2.1 Introduction 41 -- 2.2 Fuzzy Numbers 41 -- 2.3 Fuzzy Intervals 42 -- 2.4 Zadeh́<U+0099>s Extension Principle 43 -- 2.4.1 Extension Principle for Two Variables 44 -- 2.5 Fuzzy Numbers with <U+00ce>ł-Levels 48 -- 2.6 Operations on Fuzzy Numbers with Intervals 52 -- 2.7 Operations with Fuzzy Numbers based on <U+00ce>ł-Levels 54 -- 2.8 Operations on Fuzzy Numbers Using Extension Principle 62 -- 2.8.1 Operations 63 -- 2.8.2 Examples on Operations of Fuzzy Numbers Using Extension Principle 64 -- 2.9 L-R Representation of Fuzzy Numbers 66 -- 2.10 Intuitionistic Fuzzy Numbers 73 -- 2.11 Triangular Intuitionistic Fuzzy Number 74 -- 2.12 Operations Using Triangular Intuitionistic Fuzzy Numbers 75 -- 2.13 Trapezoidal Intuitionistic Fuzzy Numbers 77 -- 2.14 Cut Set of Intuitionistic Fuzzy Number 78 -- 2.15 Distances Between Two Intuitionistic Fuzzy Numbers 80 -- 2.16 Summary 80 -- References 80 -- 3 Similarity Measures and Measures of Fuzziness 83 -- 3.1 Introduction 83 -- 3.2 Distance and Similarity Measures 83 -- 3.2.1 Distance Measure 84 -- 3.2.2 Similarity Measure 84 -- 3.3 Types of Distance Measure Between Fuzzy Sets 84.…”
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Fuzzy set and its extension : the intuitionistic fuzzy set / by Chaira, Tamalika

Table of Contents: “…Preface xiii -- Organization of the Book xv -- 1 Fuzzy/Intuitionistic Fuzzy Set Theory 1 -- 1.1 Introduction to Fuzzy Set 1 -- 1.2 Mathematical Representation of Fuzzy Sets 3 -- 1.3 Membership Function 6 -- 1.4 Fuzzy Relations 10 -- 1.5 Projection 13 -- 1.6 Composition of Fuzzy Relation 14 -- 1.7 Fuzzy Binary Relation 19 -- 1.8 Transitive Closure of Fuzzy Binary Relation 21 -- 1.9 Fuzzy Equivalence Relation 23 -- 1.10 Intuitionistic Fuzzy Set 24 -- 1.11 Construction of Intuitionistic Fuzzy Set 26 -- 1.12 Intuitionistic Fuzzy Relations 29 -- 1.13 Composition of Intuitionistic Fuzzy Relation 31 -- 1.13.1 Composition of IFR Using T-norms and T-conorms 32 -- 1.14 Intuitionistic Fuzzy Binary Relation 34 -- 1.14.1 Reflexive Property 34 -- 1.14.2 Symmetric Property 37 -- 1.14.3 Transitive Property 38 -- 1.15 Summary 39 -- References 39 -- 2 Playing with Fuzzy/Intuitionistic Fuzzy Numbers 41 -- 2.1 Introduction 41 -- 2.2 Fuzzy Numbers 41 -- 2.3 Fuzzy Intervals 42 -- 2.4 Zadeh́<U+0099>s Extension Principle 43 -- 2.4.1 Extension Principle for Two Variables 44 -- 2.5 Fuzzy Numbers with <U+00ce>ł-Levels 48 -- 2.6 Operations on Fuzzy Numbers with Intervals 52 -- 2.7 Operations with Fuzzy Numbers based on <U+00ce>ł-Levels 54 -- 2.8 Operations on Fuzzy Numbers Using Extension Principle 62 -- 2.8.1 Operations 63 -- 2.8.2 Examples on Operations of Fuzzy Numbers Using Extension Principle 64 -- 2.9 L-R Representation of Fuzzy Numbers 66 -- 2.10 Intuitionistic Fuzzy Numbers 73 -- 2.11 Triangular Intuitionistic Fuzzy Number 74 -- 2.12 Operations Using Triangular Intuitionistic Fuzzy Numbers 75 -- 2.13 Trapezoidal Intuitionistic Fuzzy Numbers 77 -- 2.14 Cut Set of Intuitionistic Fuzzy Number 78 -- 2.15 Distances Between Two Intuitionistic Fuzzy Numbers 80 -- 2.16 Summary 80 -- References 80 -- 3 Similarity Measures and Measures of Fuzziness 83 -- 3.1 Introduction 83 -- 3.2 Distance and Similarity Measures 83 -- 3.2.1 Distance Measure 84 -- 3.2.2 Similarity Measure 84 -- 3.3 Types of Distance Measure Between Fuzzy Sets 84.…”
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Resource service management in manufacturing grid system / by Tao, Fei

Table of Contents: “…Front Matter -- Introduction to Manufacturing Grid -- Resource Service Optimal-Allocation System in MGrid -- Digital Description of MGrid Resource Service -- MGrid Resource Service Match and Search -- Resource Service QoS Modeling and Evaluation -- Resource Service Trust-QoS Evaluation -- Resource Service Optimal-selection and Composition Framework -- Resource Service Optimal-selection Based on Intuitionistic Fuzzy Set and Non-functionality QoS -- Correlation Relationship Management in Resource Services Composition -- Resource Service Composition Optimal-selection -- Resource Services Composition Flexibility -- Resource Services Composition Network -- Failure Detection and Recovery in Resource Service Optimal-Allocation -- Summary of the Application of Grid Technology in Manufacturing -- Cloud Manufacturing: Development and Commerce Realization of MGrid -- Bibliography -- Index -- Also of Interest.…”
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Computational intelligence in decision and control : proceedings of the 8th International FLINS Conference, Madrid, Spain, 21-24 September 2008 /

Table of Contents: “…Contrast computing using Atanassov's intuitionistic fuzzy sets / H. Bustince ... [et al.]. A new approach to ranking alternatives expressed via intuitionistic fuzzy sets / E. …”
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Fog, edge, and pervasive computing in intelligent IoT driven applications /

Table of Contents: “…Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use / Afroj Alam, Sahar Qazi, Naiyar Iqbal, Khalid Raza -- Future Opportunistic Fog/Edge Computational Models and their Limitations / Sonia Singla, Naveen Kumar Bhati, S Aswath -- Automating Elicitation Technique Selection using Machine Learning / Hatim M Elhassan Ibrahim Dafallaa, Nazir Ahmad, Mohammed Burhanur Rehman, Iqrar Ahmad, Rizwan khan -- Machine Learning Frameworks and Algorithms for Fog and Edge Computing / Murali Mallikarjuna Rao Perumalla, Sanjay Kumar Singh, Aditya Khamparia, Anjali Goyal, Ashish Mishra -- Integrated Cloud Based Library Management in Intelligent IoT driven Applications / Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, M Rubaiyat Hossain Mondal -- A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer / Nikita, Harsh Sadawarti, Balwinder Kaur, Jimmy Singla -- Location Driven Edge Assisted Device and Solutions for Intelligent Transportation / Saravjeet Singh, Jaiteg Singh -- Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator / Natasha Tiwari, Anil Kumar, Pallavi Asthana, Sumita Mishra, Bramah Hazela -- IoT Driven Healthcare Monitoring System / Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, M Rubaiyat Hossain Mondal -- Fog Computing as Future Perspective in Vehicular Ad hoc Networks / Harjit Singh, Vijay Laxmi, Arun Malik, Isha -- An Overview to Design an Efficient and Secure Fog-assisted Data Collection Method in the Internet of Things / Sofia, Arun Malik, Isha, Aditya Khamparia -- Role of Fog Computing Platform in Analytics of Internet of Things- Issues, Challenges and Opportunities / Mamoon Rashid, Umer Iqbal Wani -- A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets / Prabjot Kaur, Maria Jamal -- Security Attacks in Internet of Things / Rajit Nair, Preeti Sharma, Dileep Kumar Singh -- Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery / Inderpreet Kaur, Kamaljit Singh Saini, Jaiteg Singh Khaira -- Fruit Fly Optimization Algorithm for Intelligent IoT Applications / Satinder Singh Mohar, Sonia Goyal, Ranjit Kaur -- Optimization Techniques for Intelligent IoT Applications / Priyanka Pattnaik, Subhashree Mishra, Bhabani Shankar Prasad Mishra -- Optimization Techniques for Intelligent IoT Applications in Transport Processes / Muzafer Sara♯⁻evi♯⁷, Zoran Lon♯⁻arevi♯⁷, Adnan Hasanovi♯⁷ -- Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities / Priyanka Rajan Kumar, Sonia Goel -- Security and Privacy Issues in Fog/Edge/Pervasive Computing / Shweta Kaushik, Charu Gandhi -- Fog and Edge Driven Security & Privacy Issues in IoT Devices / Deepak Kumar Sharma, Aarti Goel, Pragun Mangla.…”
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Engineering reliability and risk assessment /

Table of Contents: “…Preliminary concepts -- 2.1 Intuitionistic fuzzy set(IFS) -- 2.2 Triangle intuitionistic fuzzy set(TIFS) -- 2.3 Algebraic t-norm(TA) and t-conorm(SA) -- 2.4 The fuzzy arithmetic operations defined on TIFS [29] -- 2.5 Failure probability evaluation for OR and AND nodes [6,30] -- 3. …”
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Supplier selection : an MCDA-based approach / by Mukherjee, Krishnendu

Table of Contents: “…1.4.11 Rank Reversal Problem in TOPSIS1.4.12 TOPSIS and Other Methods; 1.4.13 Application of TOPSIS; 1.4.14 VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje; in Serbian); 1.5 Uncertainty Analysis with MCDA; 1.5.1 Fuzzy Set-An Introduction; 1.5.2 Cascaded Fuzzy Inference System; 1.5.3 Intuitionistic Fuzzy Set (IFS)-An Introduction; 1.5.4 Dealing Uncertainty with AHP; 1.5.5 Dealing Uncertainty with TOPSIS; 1.5.6 Dealing Uncertainty with VIKOR; 1.5.7 Fuzzy AHP by Hand Calculation; 1.6 Conclusion; References; 2 Modeling and Optimization of Traditional Supplier Selection.…”
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Monitoring and control of information-poor systems / an approach based on fuzzy relational models. by Dexter, A. L.

Table of Contents: “…3.2.2 Generating Fuzzy Random Variables from a Knowledge of the Random and Systematic Errors -- 3.3 A Hybrid Approach to the Propagation of Uncertainty -- 3.4 Fuzzy Sensor Fusion Based on the Extension Principle -- 3.5 Fuzzy Sensors -- 3.6 Summary -- References -- 4 Accounting for Modelling Errors in Fuzzy Models -- 4.1 An Introduction to Rule-Based Models -- 4.2 Linguistic Fuzzy Models -- 4.2.1 Fuzzy Rules -- 4.2.2 Fuzzy Inferencing -- 4.2.3 Compositional Rules of Inference -- 4.3 Functional Fuzzy Models -- 4.4 Fuzzy Neural Networks -- 4.5 Methods of Generating Fuzzy Models -- 4.5.1 Modifying Expert Rules to Take Account of Uncertainty -- 4.5.2 Identifying Fuzzy Rules from Data -- 4.6 Defuzzification -- 4.7 Summary -- References -- 5 Fuzzy Relational Models -- 5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models -- 5.2 Fuzzy FRMs -- 5.3 Methods of Estimating Rule Confidences from Data -- 5.4 Estimating Probability Density Functions from Data -- 5.4.1 Probabilistic Interpretation of RSK Fuzzy Identification -- 5.4.2 Effect of Structural Errors on the Output of a Fuzzy FRM -- 5.4.3 Estimation Based on Limited Amounts of Training Data -- 5.5 Generic Fuzzy Models -- 5.5.1 Identification of Generic Fuzzy Models -- 5.5.2 Reducing the Time Required to Generate the Training Data -- 5.6 Summary -- References -- II CONTROL OF INFORMATION-POOR SYSTEMS -- 6 Fuzzy Decision-Making -- 6.1 Risk Assessment in Information-Poor Systems -- 6.2 Fuzzy Optimization in Information-Poor Systems -- 6.2.1 Fuzzy Goals and Fuzzy Constraints -- 6.2.2 Fuzzy Aggregation Operators -- 6.2.3 Fuzzy Ranking -- 6.3 Multi-Stage Decision-Making -- 6.3.1 Fuzzy Dynamic Programming -- 6.3.2 Branch and Bound -- 6.3.3 Genetic Algorithms -- 6.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets -- 6.4.1 Definition of an Intuitionistic Fuzzy Set.…”
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Mathematics in computational science and engineering /

Table of Contents: “…3.2 Preliminaries on Three-Way Approximation of Fuzzy Sets -- 3.2.1 Shadowed Set Approximation -- 3.2.2 Decision-Theoretic Three-Way Approximation -- 3.3 Theoretical Foundations of Shadowed Sets -- 3.3.1 Uncertainty Balance Models -- 3.3.1.1 Pedrycz's (Pd) Model -- 3.3.1.2 Tahayori-Sadeghian-Pedrycz (TSP) Model -- 3.3.1.3 Ibrahim-William-West-Kana-Singh (IWKS) Model -- 3.3.2 Minimum Error or Deng-Yao (DY) Model -- 3.3.3 Average Uncertainty or Ibrahim-West (IW) Model -- 3.3.4 Nearest Quota of Uncertainty (WIK) Model -- 3.3.5 Algorithm for Constructing Shadowed Sets -- 3.4 Principles for Constructing Decision-Theoretic Approximation -- 3.4.1 Deng and Yao Special Decision-Theoretic (DYSD) Model -- 3.4.2 Zhang, Xia, Liu and Wang (ZXLW) Generalized Decision-Theoretic Model -- 3.4.3 A General Perspective to Decision-Theoretic Three-Way Approximation -- 3.4.3.1 Determination of n, m and p for Decision-Theoretic Three-Way Approximation -- 3.4.3.2 A General Decision-Theoretic Three-Way Approximation Partition Thresholds -- 3.4.4 Example on Decision-Theoretic Three-Way Approximation -- 3.5 Concluding Remarks and Future Directions -- References -- 4 Intuitionistic Fuzzy Rough Sets: Theory to Practice -- 4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Rough Set Theory -- 4.2.2 Intuitionistic Fuzzy Set Theory -- 4.2.3 Intuitionistic Fuzzy-Rough Set Theory -- 4.3 Intuitionistic Fuzzy Rough Sets -- 4.4 Extension and Hybridization of Intuitionistic Fuzzy Rough Sets -- 4.4.1 Extension -- 4.4.1.1 Dominance-Based Intuitionistic Fuzzy Rough Sets -- 4.4.1.2 Covering-Based Intuitionistic Fuzzy Rough Sets -- 4.4.1.3 Kernel Intuitionistic Fuzzy Rough Sets -- 4.4.1.4 Tolerance-Based Intuitionistic Fuzzy Rough Sets -- 4.4.1.5 Interval-Valued Intuitionistic Fuzzy Rough Sets -- 4.4.2 Hybridization -- 4.4.2.1 Variable Precision Intuitionistic Fuzzy Rough Sets.…”
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Proceedings of the International Conference [on] Information Computing and Automation, University of Electronic Science and Technology of China, China, 20-22 December 2007.

Table of Contents: “…Hashimoto -- Generalizing TOPSIS method for multiple attribute decision making in intuitionistic fuzzy setting / G.W. Wei and H.B. Cao -- Design and accomplishment of intelligent control on learning process for ICAI / X.L. …”
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