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New types of Neutrosophic Set/Logic/Probability, Neutrosophic Over-/Under-/Off-Set, Neutrosophic Refined Set, and their Extension to Plithogenic Set/Logic/Probability, with Applications
| New types of Neutrosophic Set/Logic/Probability, Neutrosophic Over-/Under-/Off-Set, Neutrosophic Refined Set, and their Extension to Plithogenic Set/Logic/Probability, with Applications |
| Autore | Smarandache Florentin |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 online resource (714 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
?-level
accuracy function aggregation aggregation operations arithmetic averaging operator binad BNHHA aggregation operator BNHOWA aggregation operator BNHWA aggregation operator certainty function Choquet integral classical statistics clifford semigroup combined weighted average complex neutrosophic set complex neutrosophic soft expert set consumer's risk covering cubic sets De Morgan neutrosophic triples decision making decision-making dietary fat level distance measure e-marketing Einstein t-norm exponential similarity measure extended non-standard analysis extended nonstandard analysis extended nonstandard neutrosophic logic financial assets Function approximation fuzzy logic fuzzy numbers fuzzy parameterized single valued neutrosophic soft expert set generalized neutrosophic extended triplet group graph representation group decision making hypergroup idempotents implicator infinitely ?-distributive infinitesimals infinities Internet of Things intuitionistic fuzzy parameters left monad closed to the right logarithmic aggregation operators logarithmic operational laws low-carbon supplier selection MAGDM matrix representation maximizing deviation MCGDM problems membership function MoBiNad set monad multi-attribute decision making Multi-attribute decision making multi-attribute decision-making multi-attribute decision-making (MADM) multi-attribute group decision making multi-criteria decision making techniques multi-granulation neutrosophic rough set multicriteria decision-making n-person cooperative game NET-hypergroup Neutrosophic compound orthogonal neural network neutrosophic correlation neutrosophic cubic Einstein ordered weighted geometric operator (NCEOWG) neutrosophic cubic Einstein weighted geometric operator (NCEWG) neutrosophic cubic hybrid weighted arithmetic and geometric aggregation operator (NCHWAGA) neutrosophic cubic ordered weighted geometric operator (NCOWG) neutrosophic cubic sets neutrosophic cubic soft expert system neutrosophic cubic soft sets neutrosophic cubic weighted geometric operator (NCWG) neutrosophic extended triplet group neutrosophic extended triplet semihypergroup (NET-semihypergroup) Neutrosophic function neutrosophic goal programming approach neutrosophic logical relationship neutrosophic logical relationship groups Neutrosophic number neutrosophic numbers neutrosophic offconorm neutrosophic offnorm neutrosophic offset neutrosophic offuninorm neutrosophic quadruple numbers neutrosophic quadruple rings neutrosophic regression neutrosophic residual implications neutrosophic rings neutrosophic set neutrosophic sets neutrosophic soft rough neutrosophic statistical interval method neutrosophic statistics neutrosophic symmetric scenarios neutrosophic time series neutrosophic topology neutrosophic triangular norms neutrosophic weight neutrsophic set non-dual non-standard analysis non-standard neutrosophic mobinad set non-standard neutrosophic topology nonstandard analysis nonstandard arithmetic operations nonstandard neutrosophic infimum nonstandard neutrosophic lattices of first type (as poset) and second type (as algebraic structure) nonstandard neutrosophic logic nonstandard neutrosophic supremum nonstandard reals nonstandard unit interval numerical application objectness open and closed monads to the left/right optimization solution ordinary single valued neutrosophic (co)topology ordinary single valued neutrosophic base ordinary single valued neutrosophic neighborhood system ordinary single valued neutrosophic subbase ordinary single valued neutrosophic subspace paper defect diagnosis performance indicators pierced and unpierced binads pierced binad plithogeny port evaluation probabilistic neutrosophic hesitant fuzzy set (PNHFS) producer's risk producer's risk' prospector prostate cancer Q-neutrosophic set Q-neutrosophic soft set quality function deployment quasi-completely regular semigroup refined neutrosophic numbers refined neutrosophic quadruple numbers relations representable neutrosophic t-norms residuated lattices right monad closed to the left rough set approximation sample size sampling plan score function semihypergroup shale gas water management system simplified neutrosophic hesitant fuzzy set simplified neutrosophic set single valued neutrosophic set single valued neutrosophic sets single-valued neutrosophic linguistic set single-valued neutrosophic set single-valued neutrosophic soft number and its operations smart port soft expert set soft set soft sets standard reals supply chain sustainability metrics SVN soft weighted arithmetic averaging operator SVN soft weighted geometric averaging operator symmetric relation TOPSIS triangular neutrosophic cubic fuzzy number triangular neutrosophic number two universes uncertainty modeling uninorm unpierced binad visual tracking weighted average operator weighted geometric operator weighted multiple instance learning |
| ISBN | 3-03921-939-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367741303321 |
Smarandache Florentin
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Theory and Applications of Ordered Fuzzy Numbers [[electronic resource] ] : A Tribute to Professor Witold Kosiński / / edited by Piotr Prokopowicz, Jacek Czerniak, Dariusz Mikołajewski, Łukasz Apiecionek, Dominik Ślȩzak
| Theory and Applications of Ordered Fuzzy Numbers [[electronic resource] ] : A Tribute to Professor Witold Kosiński / / edited by Piotr Prokopowicz, Jacek Czerniak, Dariusz Mikołajewski, Łukasz Apiecionek, Dominik Ślȩzak |
| Autore | Łukasz Apiecionek |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Springer Nature, 2017 |
| Descrizione fisica | 1 online resource (XVIII, 322 p. 156 illus., 106 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Studies in Fuzziness and Soft Computing |
| Soggetto topico |
Computational intelligence
Control engineering Operations research Decision making Management science Computational Intelligence Control and Systems Theory Operations Research/Decision Theory Operations Research, Management Science |
| Soggetto non controllato |
fuzzy prediction models
uncertainty modeling trend processing propagation of uncertainty fuzzy arithmetic analysis defuzzyfication Kosinski’s fuzzy numbers |
| ISBN | 3-319-59614-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Memories of Professor Witold Kosiński -- Scientific Development -- Scientific and Academic Achievements (Part I) -- Scientific and Academic Achievements (Part II) -- Scientific Collaboration -- Teaching and Supervision -- Scientific and Social Services -- Personality and Memoires -- Acknowledgements -- Contents -- Part I Background of Fuzzy Set Theory -- 1 Introduction to Fuzzy Sets -- 1.1 Classic and Fuzzy Sets -- 1.2 Fuzzy Sets---Basic Definitions -- 1.3 Extension Principle -- 1.4 Fuzzy Relations -- 1.5 Cylindrical Extension and Projection of a Fuzzy Set -- 1.6 Fuzzy Numbers -- 1.7 Summary -- References -- 2 Introduction to Fuzzy Systems -- 2.1 Introduction -- 2.2 Fuzzy Conditional Rules -- 2.3 Approximate Reasoning -- 2.3.1 Compositional Rule of Inference -- 2.3.2 Approximate Reasoning with Knowledge Base -- 2.3.3 Fuzzification and Defuzzification -- 2.4 Basic Types of Fuzzy Systems -- 2.4.1 Mamdani--Assilan Fuzzy Model -- 2.4.2 Takagi--Sugeno--Kang Fuzzy System -- 2.4.3 Tsukamoto Fuzzy System -- 2.5 Summary -- References -- Part II Theory of Ordered Fuzzy Numbers -- 3 Ordered Fuzzy Numbers: Sources and Intuitions -- 3.1 Introduction -- 3.2 Problems with Calculations on Fuzzy Numbers -- 3.3 Related Work -- 3.4 Decomposition of Fuzzy Memberships -- 3.5 Idea of Ordered Fuzzy Numbers -- 3.6 Summary -- References -- 4 Ordered Fuzzy Numbers: Definitions and Operations -- 4.1 Introduction -- 4.2 The Ordered Fuzzy Number Model -- 4.3 Basic Notions for OFNs -- 4.3.1 Standard Representation of OFNs -- 4.3.2 OFN Support -- 4.3.3 OFN Membership Function -- 4.3.4 Real Numbers as OFN Singletons -- 4.4 Improper OFNs -- 4.5 Basic Operations on OFNs -- 4.5.1 Addition and Subtraction -- 4.5.2 Multiplication and Division -- 4.5.3 General Model of Operations -- 4.5.4 Solving Equations -- 4.6 Interpretations of OFNs.
4.6.1 Direction as a Trend -- 4.6.2 Validity of Operations -- 4.6.3 The Meaning of Improper OFNs -- 4.7 Summary and Further Intuitions -- References -- 5 Processing Direction with Ordered Fuzzy Numbers -- 5.1 Introduction -- 5.2 Direction Measurement Tool -- 5.2.1 The PART Function -- 5.2.2 The Direction Determinant -- 5.3 Compatibility Between OFNs -- 5.4 Inference Sensitive to Direction -- 5.4.1 Directed Inference Operation -- 5.4.2 Examples -- 5.5 Aggregation of OFNs -- 5.5.1 The Aggregation's Basic Properties -- 5.5.2 Arithmetic Mean Directed Aggregation -- 5.5.3 Aggregation for Premise Parts of Fuzzy Rules -- 5.6 Summary -- References -- 6 Comparing Fuzzy Numbers Using Defuzzificators on OFN Shapes -- 6.1 Introduction -- 6.2 Formal Approach to the Problem -- 6.3 Defuzzification Methods -- 6.3.1 Defuzzification Methods for OFN -- 6.4 Definition of Golden Ratio Defuzzification Operator -- 6.4.1 Golden Ratio for OFN -- 6.5 Golden Ratio -- 6.6 Defuzzification Conditions for GR -- 6.6.1 Normalization -- 6.6.2 Restricted Additivity -- 6.6.3 Homogeneity -- 6.7 Definition of Mandala Factor Defuzzification Operator -- 6.8 Mandala Factor -- 6.9 Defuzzification Conditions for MF -- 6.9.1 Normalization -- 6.9.2 Restricted Additivity -- 6.9.3 Homogeneity -- 6.10 Catalogue of the Shapes of Numbers in OFN Notation -- 6.11 Conclusion -- References -- 7 Two Approaches to Fuzzy Implication -- 7.1 Introduction -- 7.2 Lattice Structure and Implications on SOFNs -- 7.2.1 Step-Ordered Fuzzy Numbers -- 7.2.2 Lattice on mathcalRK -- 7.2.3 Complements and Negation on calN -- 7.2.4 Fuzzy Implication on BSOFN -- 7.2.5 Applications -- 7.3 Metasets -- 7.3.1 The Binary Tree T and the Boolean Algebra mathfrakB -- 7.3.2 General Definition of Metaset -- 7.3.3 Interpretations of Metasets -- 7.3.4 Forcing -- 7.3.5 Set-Theoretic Relations for Metasets. 7.3.6 Applications of Metasets -- 7.3.7 Classical and Fuzzy Implication -- 7.4 Conclusions and Further Research -- References -- Part III Examples of Applications -- 8 OFN Capital Budgeting Under Uncertainty and Risk -- 8.1 Introduction -- 8.2 Ordered Fuzzy Numbers -- 8.3 Classic Capital Budgeting Methods -- 8.4 Fuzzy Approach to the Discount Methods -- 8.5 Computational Example of the Investment Project -- 8.6 Summary -- References -- 9 Input-Output Model Based on Ordered Fuzzy Numbers -- 9.1 Introduction -- 9.2 Input-Output Analysis -- 9.3 Example of Application of OFNs in the Leontief Model -- 9.4 Conclusions -- References -- 10 Ordered Fuzzy Candlesticks -- 10.1 Introduction -- 10.2 Ordered Fuzzy Candlesticks -- 10.3 Volume and Spread -- 10.3.1 Volume -- 10.3.2 Spread -- 10.4 Ordered Fuzzy Candlesticks in Technical Analysis -- 10.4.1 Ordered Fuzzy Technical Analysis Indicators -- 10.4.2 Ordered Fuzzy Candlestick as Technical Analysis Indicator -- 10.5 Ordered Fuzzy Time Series Models -- 10.6 Conclusion and Future Works -- References -- 11 Detecting Nasdaq Composite Index Trends with OFNs -- 11.1 Introduction -- 11.2 Application of OFN Notation for the Fuzzy Observation of NASDAQ Composite -- 11.3 Ordered Fuzzy Number Formulas -- 11.4 Conclusions -- References -- 12 OFNAnt Method Based on TSP Ant Colony Optimization -- 12.1 Introduction -- 12.2 Application of Ant Colony Algorithms in Searching for the Optimal Route -- 12.3 OFNAnt, a New Ant Colony Algorithm -- 12.4 Experiment -- 12.4.1 Experiment Execution Method -- 12.4.2 Software Used for Experiment -- 12.4.3 Experimental Data -- 12.5 Results of Experiment -- 12.6 Summary and Conclusions -- References -- 13 A New OFNBee Method as an Example of Fuzzy Observance Applied for ABC Optimization -- 13.1 Introduction -- 13.2 ABC (Artificial Bee Colony) Model -- 13.3 Selected OFN Issues. 13.4 New Hybrid OFNBee Method -- 13.5 Experimental Results -- 13.6 Conclusion -- References -- 14 Fuzzy Observation of DDoS Attack -- 14.1 Introduction -- 14.2 DDoS Attack Description and Recognition -- 14.3 The Idea of Attack Recognition and Prevention -- 14.4 Attack Observation Using OFNs -- 14.5 Experiment Test Results -- 14.5.1 Test Description -- 14.5.2 Attack Detection Using Proposed Method -- 14.6 Conclusions-Method Comparision -- References -- 15 Fuzzy Control for Secure TCP Transfer -- 15.1 Introduction -- 15.2 Multipath TCP -- 15.3 Multipath TCP Schedulers -- 15.3.1 Multipath TCP Standard Scheduler -- 15.3.2 Multipath TCP Secure Scheduler -- 15.3.3 Multipath TCP Scheduler with OFN Usage -- 15.3.4 OFN for Problem Detection -- 15.4 OFN Scheduler Algorithm -- 15.5 Simulation Test Results -- 15.6 Conclusions -- References -- 16 Fuzzy Numbers Applied to a Heat Furnace Control -- 16.1 Introduction -- 16.2 Selected Definitions -- 16.2.1 The Essence of Ordered Fuzzy Numbers -- 16.2.2 Fuzzy Controller -- 16.2.3 Control of the Stove on Solid Fuel -- 16.3 Classic Fuzzy Controller -- 16.4 The Controller for the OFNs -- 16.4.1 Directed OFN as a Combustion Trend -- 16.5 Modeling Trend in the Inference Process -- 16.6 Conclusions -- References -- 17 Analysis of Temporospatial Gait Parameters -- 17.1 Introduction -- 17.2 Methods -- 17.2.1 Subjects -- 17.2.2 Methods -- 17.2.3 Statistical Analysis -- 17.2.4 Fuzzy-Based Tool for Gait Assessment -- 17.2.5 Main Ideas of the OFN Model -- 17.2.6 OFN Model in Gait Assessment -- 17.3 Results -- 17.4 Discussion -- 17.5 Conclusions -- References -- 18 OFN-Based Brain Function Modeling -- 18.1 Introduction -- 18.2 State of the Art -- 18.2.1 Theory -- 18.2.2 Modeling Complex Ideas with Fuzzy Systems -- 18.2.3 Clinical Practice -- 18.2.4 Models for Linking Hypotheses and Experimental Studies -- 18.3 Concepts. 18.3.1 Data Ladder -- 18.3.2 Models of a Single Neuron -- 18.3.3 Models of Biologically Relevant Neural Networks -- 18.3.4 Models of Human Behavior -- 18.4 Traditional versus Fuzzy Approach -- 18.5 OFN as an Alternative Approach to Fuzziness -- 18.6 Patterns and Examples -- 18.6.1 Intuitive Modeling of the Complex Functions -- 18.6.2 Improving Policy Gradient Method -- 18.6.3 Modeling Learning Rate with the OFNs -- 18.7 Discussion -- 18.7.1 Results of Other Scientists -- 18.7.2 Limitations of Our Approach and Directions for Further Research -- 18.8 Conclusions -- References. |
| Record Nr. | UNINA-9910231246403321 |
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Łukasz Apiecionek
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| Springer Nature, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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