MARC details
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| Longitud fija campo de control |
09514ntdaa2200301 ab4500 |
| 003 - IDENTIFICADOR DEL NÚMERO DE CONTROL |
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| Identificador del número de control |
UnInEc |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN |
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| Fecha y hora de la última transacción |
20231220101948.0 |
| 006 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - CARACTERÍSTICAS DEL MATERIAL ADICIONAL |
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| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA |
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| Códigos de información de longitud fija |
140501s9999 mx ||||f |||| 00| 0 spa d |
| 020 ## - NÚMERO INTERNACIONAL NORMALIZADO PARA LIBROS |
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| Número Internacional Normalizado para Libros (ISBN) |
9789813238176 |
| 040 ## - FUENTE DE LA CATALOGACIÓN |
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| Centro catalogador de origen |
CIBESPAM MFL |
| 041 ## - CÓDIGO DE LENGUA |
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| Código de lengua del texto;banda sonora o título independiente |
eng |
| 082 ## - NÚMERO DE LA CLASIFICACIÓN DECIMAL DEWEY |
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| Número de clasificación |
511.3 |
| Cutter |
Z17 |
| 100 ## - PUNTO DE ACCESO PRINCIPAL-NOMBRE DE PERSONA |
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| Nombre de persona |
Zadeh, Lotfi A. |
| 245 ## - MENCIÓN DE TÍTULO |
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| Título |
Fuzzy logic theory and applications: Part I and Part II. |
| 260 ## - PUBLICACIÓN, DISTRIBUCIÓN, ETC. (PIE DE IMPRENTA) |
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| Lugar de publicación, distribución, etc. |
New Jersey |
| Nombre del editor, distribuidor, etc. |
World Scientific |
| Fecha de publicación, distribución, etc. |
2018 |
| 300 ## - DESCRIPCIÓN FÍSICA |
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| Extensión |
xxviii, 581 pages; |
| Otras características físicas |
Figures, tables; |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
Part I Fuzzy Logic Theory<br/>Chapter 1 Fuzzy Sets<br/>1.1 Introduction<br/>1.2 Definitions<br/>1.3 Some Properties of ∪, ∩, and Complementation<br/>1.4 Algebraic Operations on Fuzzy Sets<br/>1.5 Convexity<br/>1.6 Examples on Operations on Fuzzy Sets<br/>1.7 Fuzzy Arithmetic<br/>1.8 Extensions of Fuzzy Sets<br/>1.8.1 Type-2 fuzzy sets and numbers<br/>1.8.2 Intuitionistic fuzzy sets<br/>1.8.3 Rough sets<br/>1.8.4 Neutrosophic set<br/>Chapter 2 Fuzzy Logic<br/>2.1 Introduction<br/>2.2 Fuzzy Logic<br/>2.3 Approximate Reasoning<br/>2.4 Analysis of Different Fuzzy Logics<br/>2.5 Extended Fuzzy Logic<br/>2.5.1 Introduction<br/>2.5.2 f-geometry and f-transformation<br/>Chapter 3 Restriction Concept<br/>3.1 Introduction to Restriction Concept<br/>3.1.1 Computation with restrictions<br/>3.2 Truth and Meaning<br/>3.2.1 Truth qualification: Internal and external truth values<br/>Chapter 4 Fuzzy Probabilities<br/>4.1 Introduction<br/>4.2 The Concept of Fuzzy Probability<br/>Chapter 5 Fuzzy Functions<br/>5.1 Definition of Fuzzy Functions<br/>5.2 Integrability and Differentiability of Fuzzy Functions<br/>Chapter 6 Fuzzy Systems<br/>6.1 Introduction<br/>6.2 System, Aggregate and State<br/>6.3 State Equations for Fuzzy Systems<br/>6.4 Fuzzy Rule-based System<br/>Chapter 7 Z-number Theory<br/>7.1 Introduction<br/>7.2 Computation with Z-numbers<br/>7.2.1 Computation with continuous Z-numbers<br/>7.2.2 Computation with discrete Z-numbers<br/>7.3 Standard Division of Discrete Z-numbers<br/>Chapter 8 Generalized Theory of Uncertainty<br/>8.1 Introduction<br/>8.2 The Concept of NL-Computation<br/>8.3 The Concept of Precisiation<br/>8.4 The Concept of Cointensive Precisiation<br/>8.5 A Key Idea — The Meaning Postulate<br/>8.6 The Concept of a Generalized Constraint<br/>8.7 Principal Modalities of Generalized Constraints<br/>8.8 The Concept of Bimodal Constraint/Distribution<br/>8.9 The Concept of a Group Constraint<br/>8.10 Primary Constraints, Composite Constraints and Standard Constraints<br/>8.11 The Generalized Constraint Language and Standard Constraint Language<br/>8.12 The Concept of Granular Value<br/>8.13 The Concept of Protoform<br/>8.14 The Concept of Generalized-Constraint-Based Computation<br/>8.15 Protoformal Deduction Rules<br/>8.16 Examples of Computation/Deduction<br/>8.16.1 The Robert example<br/>8.16.2 The tall Swedes problem<br/>8.16.3 Tall Swedes and tall Italians<br/>8.16.4 Simplified trip planning<br/>Part II Applications and Advanced Topics of Fuzzy Logic<br/>Chapter 9 Restriction-based Semantics<br/>9.1 Precisiation of Meaning<br/>9.1.1 Canonical form of p: cf (p)<br/>9.2 The Concept of Explanatory Database (ED)<br/>Chapter 10 Granular Computing: Principles and Algorithms<br/>10.1 Introduction<br/>10.2 Information Granularity: Selected Examples<br/>10.2.1 Image processing<br/>10.2.2 Processing and interpretation of time series<br/>10.2.3 Granulation of time<br/>10.2.4 Data summarization<br/>10.3 Formal Platforms of Information Granularity<br/>10.4 Characterization of Information Granules: Coverage and Specificity<br/>10.5 The Design of Information Granules<br/>10.5.1 The principle of justifiable granularity<br/>10.5.2 Augmentations of the principle of justifiable granularity<br/>10.5.2.1 Weighted data<br/>10.5.2.2 Inhibitory data<br/>10.6 Information Granularity as a Design Asset in System Modeling<br/>10.6.1 Granular mappings<br/>10.6.2 Granular aggregation: An enhancement of aggregation operations through allocation of information granularity<br/>10.6.3 Development of granular models of higher type<br/>10.7 Concluding Comments<br/>Chapter 11 Complex Fuzzy Sets and Complex Fuzzy Logic. An Overview of Theory and Applications<br/>11.1 Introduction<br/>11.2 Complex Fuzzy Logic and Set Theory<br/>11.2.1 Complex fuzzy sets<br/>11.2.2 Complex fuzzy logic<br/>11.3 Generalized Complex Fuzzy Logic<br/>11.3.1 Propositional and first-order predicate complex fuzzy logic<br/>11.3.2 Complex fuzzy propositions and inference examples<br/>11.3.3 Complex fuzzy inference example<br/>11.4 Generalized Complex Fuzzy Class Theory<br/>11.4.1 Complex fuzzy classes and connectives examples<br/>11.5 Pure Complex Fuzzy Classes<br/>11.6 Recent Developments in the Theory and Applications of CFL and CFS<br/>11.6.1 Advances in the theoretical foundations of CFL/CFS<br/>11.6.2 Applications of CFL/CFS<br/>11.7 Conclusion<br/>Chapter 12 Introduction to Fuzzy Logic Control<br/>12.1 Introduction<br/>12.2 The Mamdani Fuzzy Controller<br/>12.2.1 Fuzzification module<br/>12.2.2 Fuzzy rules<br/>12.2.3 Fuzzy inference mechanism and defuzzification<br/>12.3 Design of Fuzzy Controllers<br/>12.3.1 Selection of membership functions<br/>12.3.2 Rule-base<br/>12.3.3 Implementation<br/>12.4 Multiple-Output, Single-Input (MISO) Mamdani Fuzzy Controllers<br/>12.5 Takagi-Sugeno (TS) Fuzzy Controllers<br/>12.6 Fuzzy Control Versus Conventional Control<br/>12.6.1 Advantages of fuzzy control<br/>12.6.2 Disadvantages of fuzzy control<br/>12.7 Applicability of Fuzzy Control<br/>Chapter 13 Fuzzy Decision-Making<br/>13.1 Introduction<br/>13.2 Definitions<br/>13.3 Decision Model<br/>13.4 Examples<br/>13.4.1 Zadeh’s two boxes problem<br/>13.4.2 Investment problem<br/>Chapter 14 Selected Interpretability Aspects of Fuzzy Systems for Classification<br/>14.1 Introduction<br/>14.1.1 Attempts at systematizing solutions for interpretability of fuzzy systems<br/>14.1.2 Solutions proposed in this chapter<br/>14.2 Description of a Fuzzy System for Classification<br/>14.2.1 Rule base<br/>14.2.2 Defuzzification process<br/>14.2.3 Aggregation and inference operators<br/>14.3 A Hybrid Genetic-Imperialist Algorithm for Automatic Selection of Structure and Parameters of a Fuzzy System<br/>14.3.1 Encoding of potential solutions<br/>14.3.2 Evaluation of potential solutions<br/>14.3.3 Processing of potential solutions<br/>14.4 Interpretability Criteria of a Fuzzy System for Classification<br/>14.4.1 Complexity evaluation criterion<br/>14.4.2 Fuzzy sets readability evaluation criterion<br/>14.4.2.1 Criterion for assessing similarity of fuzzy sets width<br/>14.4.3 Fuzzy rules readability evaluation criteria<br/>14.4.3.1 Criterion for assessing fuzzy rules activity<br/>14.4.4 Criterion for assessing the readability of weights values in the fuzzy rule base<br/>14.4.5 Criterion for assessing the readability of aggregation and inference operators<br/>14.4.6 Criterion for assessing the defuzzification mechanism<br/>14.5 Simulations<br/>Chapter 15 Fuzzy Reinforcement Learning<br/>15.1 The GARIC Architecture<br/>15.2 The ACFRL Algorithm<br/>15.3 Fuzzy Q-Learning to Solve Fuzzy Dynamic Programming<br/>Chapter 16 Adaptive Neuro-Fuzzy Inference Systems (ANFISs)<br/>16.1 Introduction<br/>16.2 ANFIS Architecture<br/>16.3 Hybrid Learning Algorithm<br/>16.4 Learning Methods That Cross-Fertilize ANFIS and RBFN<br/>16.5 ANFIS as a Universal Approximator<br/>16.5.1 Stone-Weierstrass theorem<br/>16.5.2 Algebraic closure — Multiplicative<br/>16.6 Simulation Examples<br/>16.6.1 Practical considerations<br/>16.6.2 Example 1: Modeling a two-input sinc function<br/>16.6.3 Example 2: Modeling a three-input nonlinear function<br/>16.6.4 Example 3: Online identification in control systems<br/>16.6.5 Example 4: Predicting chaotic time series<br/>16.6.6 Example 5: Dimensionality reduction for ANFIS<br/>16.7 Extensions and Advanced Topics<br/>Chapter 17 Fuzzy Expert Systems<br/>17.1 Introduction<br/>17.2 Fuzzy Expert Systems Using Bayes-Shortliffe Approach<br/>17.2.1 Structure of the system<br/>17.2.2 Knowledge representation<br/>17.2.3 Inference<br/>17.3 Examples<br/>17.3.1 The expert system for scheduling of oil-refinery production<br/>17.3.2 Fuzzy hypotheses generating and accounting systems<br/>17.3.3 Forecasting of conflicts<br/>Chapter 18 Application of Logistic Regression Analysis to Fuzzy Cognitive Maps<br/>18.1 Introduction<br/>18.2 Fuzzy Cognitive Maps<br/>18.3 Logistic and Multinomial Logistic Regression Analysis<br/>18.4 Application Examples<br/>18.4.1 The city-health model<br/>18.4.2 The liquid tank model<br/>18.5 Conclusions<br/>Chapter 19 Fuzzy Logic in Medicine<br/>19.1 Introduction<br/>19.2 Fuzzy Signal Processing-Trans-Skull Brain Imaging<br/>19.2.1 Characteristics with respect to echo shape, λf<br/>19.2.2 Characteristics with respect to magnitude of echo amplitude, λa<br/>19.2.3 Characteristic value with respect to location, λth<br/>19.3 Health Checkup Data Analysis<br/>Bibliography<br/>Index |
| 520 ## - NOTA DE SUMARIO |
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| Sumario, etc, |
Nowadays, voluminous textbooks and monographs in fuzzy logic are devoted only to separate or some combination of separate facets of fuzzy logic. There is a lack of a single book that presents a comprehensive and self-contained theory of fuzzy logic and its applications.Written by world renowned authors, Lofti Zadeh, also known as the Father of Fuzzy Logic, and Rafik Aliev, who are pioneers in fuzzy logic and fuzzy sets, this unique compendium includes all the principal facets of fuzzy logic such as logical, fuzzy-set-theoretic, epistemic and relational. Theoretical problems are prominently illustrated and illuminated by numerous carefully worked-out and thought-through examples.This invaluable volume will be a useful reference guide for academics, practitioners, graduates and undergraduates in fuzzy logic and its applications. |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Fuzzy Logic |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Fuzzy Mathematics |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Logic |
| 650 ## - PUNTO DE ACCESO ADICIONAL DE MATERIA - TÉRMINO DE MATERIA |
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| Término de materia o nombre geográfico como elemento inicial |
Fuzzy Probability |
| 700 ## - PUNTO DE ACCESO ADICIONAL - NOMBRE DE PERSONA |
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| Nombre de persona |
Aliev, Rafik A. |
| 912 ## - DATOS OPENBIBLIO |
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| Fecha de última modificación |
2023-12-20 |
| Usuario que lo modifico por última vez |
Paúl Villacreses |
| 913 ## - ÁREA Y CARRERA |
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| Área de Conocimiento |
Información y Comunicación (TIC) |
| Carrera |
Carrera de Computación |
| Líneas de Investigación Institucionales |
Generación de tecnología para la soberanía alimentaria |
| 942 ## - ENTRADA DE ELEMENTOS AGREGADOS (KOHA) |
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| Fuente de clasificaión o esquema |
Dewey Decimal Classification |
| Koha [por defecto] tipo de item |
Libros |