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Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide
| Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide |
| Autore | Pedrycz Witold <1953-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley : , c2007 |
| Descrizione fisica | 1 online resource (550 p.) |
| Disciplina |
006.3
620.00113 |
| Altri autori (Persone) | GomideFernando |
| Soggetto topico |
Soft computing
Fuzzy systems |
| ISBN |
1-281-00192-9
9786611001926 0-470-16896-X 0-470-16895-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface -- 1 Introduction -- 1.1 Digital communities and a fundamental quest for human-centric systems -- 1.2 A historical overview: towards a non-Aristotelian 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 Interval-valued fuzzy sets -- 8.4 Type-2 fuzzy sets -- 8.5 Shadowed sets as a three-valued 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 s-cuts -- 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 Rule-based 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 Rule-Based Systems and Architectures -- 11.6 Approximation properties of fuzzy rule-based models -- 11.7 Development of Rule-Based Systems -- 11.8 Parameter estimation procedure for functional rule-based systems -- 11.9 Design issues of rule-based systems - consistency, completeness, and the curse of dimensionality -- 11.10 The curse of dimensionality in rule-based systems -- 11.11 Development scheme of fuzzy rule-based 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 Uninorm-based 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 rule-based 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 cluster-based representation of the input - output mappings -- 14.2 Context-based 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 Human-centric 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. |
| Record Nr. | UNINA-9910144575803321 |
Pedrycz Witold <1953->
|
||
| Hoboken, New Jersey : , : John Wiley : , c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide
| Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide |
| Autore | Pedrycz Witold <1953-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley : , c2007 |
| Descrizione fisica | 1 online resource (550 p.) |
| Disciplina |
006.3
620.00113 |
| Altri autori (Persone) | GomideFernando |
| Soggetto topico |
Soft computing
Fuzzy systems |
| ISBN |
1-281-00192-9
9786611001926 0-470-16896-X 0-470-16895-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface -- 1 Introduction -- 1.1 Digital communities and a fundamental quest for human-centric systems -- 1.2 A historical overview: towards a non-Aristotelian 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 Interval-valued fuzzy sets -- 8.4 Type-2 fuzzy sets -- 8.5 Shadowed sets as a three-valued 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 s-cuts -- 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 Rule-based 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 Rule-Based Systems and Architectures -- 11.6 Approximation properties of fuzzy rule-based models -- 11.7 Development of Rule-Based Systems -- 11.8 Parameter estimation procedure for functional rule-based systems -- 11.9 Design issues of rule-based systems - consistency, completeness, and the curse of dimensionality -- 11.10 The curse of dimensionality in rule-based systems -- 11.11 Development scheme of fuzzy rule-based 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 Uninorm-based 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 rule-based 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 cluster-based representation of the input - output mappings -- 14.2 Context-based 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 Human-centric 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. |
| Record Nr. | UNINA-9910830241003321 |
|
Pedrycz Witold <1953->
|
||
| Hoboken, New Jersey : , : John Wiley : , c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fuzzy systems modeling in environmental and health risk assessment / / edited by Boris Faybishenko, Rehan Sadiq and Ashok Deshpande
| Fuzzy systems modeling in environmental and health risk assessment / / edited by Boris Faybishenko, Rehan Sadiq and Ashok Deshpande |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] |
| Descrizione fisica | 1 online resource (332 pages) |
| Disciplina | 511.313 |
| Collana | Water Resources Monograph Series |
| Soggetto topico |
Fuzzy systems
Environmental risk assessment - Mathematical models Environmental management - Mathematical models Health risk assessment - Mathematical models Fuzzy systems in medicine |
| ISBN |
1-119-56950-8
1-119-56949-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830346603321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genetic fuzzy systems [[electronic resource] ] : evolutionary tuning and learning of fuzzy knowledge bases / / Oscar Cordón ... [et al.]
| Genetic fuzzy systems [[electronic resource] ] : evolutionary tuning and learning of fuzzy knowledge bases / / Oscar Cordón ... [et al.] |
| Pubbl/distr/stampa | Singapore ; ; River Edge, NJ, : World Scientific, c2001 |
| Descrizione fisica | 1 online resource (489 p.) |
| Disciplina | 006.31 |
| Altri autori (Persone) | CordónOscar |
| Collana | Advances in fuzzy systems |
| Soggetto topico |
Fuzzy systems
Genetics - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9786611956370
1-281-95637-6 981-281-073-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Foreword; Preface; Contents; Chapter 1 Fuzzy Rule-Based Systems; 1.1 Framework: Fuzzy Logic and Fuzzy Systems; 1.2 Mamdani Fuzzy Rule-Based Systems; 1.3 Takagi-Sugeno-Kang Fuzzy Rule-Based Systems; 1.4 Generation of the Fuzzy Rule Set; 1.5 Applying Fuzzy Rule-Based Systems; Chapter 2 Evolutionary Computation; 2.1 Conceptual Foundations of Evolutionary Computation; 2.2 Genetic Algorithms; 2.3 Other Evolutionary Algorithms; Chapter 3 Introduction to Genetic Fuzzy Systems; 3.1 Soft Computing; 3.2 Hybridisation in Soft Computing; 3.3 Integration of Evolutionary Algorithms and Fuzzy Logic
3.4 Genetic Fuzzy SystemsChapter 4 Genetic Tuning Processes; 4.1 Tuning of Fuzzy Rule-Based Systems; 4.2 Genetic Tuning of Scaling Functions; 4.3 Genetic Tuning of Membership Functions of Mamdani Fuzzy Rule-Based Systems; 4.4 Genetic Tuning of TSK Fuzzy Rule Sets; Chapter 5 Learning with Genetic Algorithms; 5.1 Genetic Learning Processes. Introduction; 5.2 The Michigan Approach. Classifier Systems; 5.3 The Pittsburgh Approach; 5.4 The Iterative Rule Learning Approach; Chapter 6 Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach; 6.1 Basic Features of Fuzzy Classifier Systems 6.2 Fuzzy Classifier Systems for Learning Rule Bases6.3 Fuzzy Classifier Systems for Learning Fuzzy Rule Bases; Chapter 7 Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach; 7.1 Coding Rule Bases as Chromosomes; 7.2 Multi-chromosome Genomes (Coding Knowledge Bases); 7.3 Examples; Chapter 8 Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach; 8.1 Coding the Fuzzy Rules; 8.2 Learning Fuzzy Rules under Competition; 8.3 Post-Processing: Refining Rule Bases under Cooperation; 8.4 Inducing Cooperation in the Fuzzy Rule Generation Stage; 8.5 Examples Chapter 9 Other Genetic Fuzzy Rule-Based System Paradigms9.1 Designing Fuzzy Rule-Based Systems with Genetic Progamming; 9.2 Genetic Selection of Fuzzy Rule Sets; 9.3 Learning the Knowledge Base via the Genetic Derivation of the Data Base; 9.4 Other Genetic-Based Machine Learning Approaches; Chapter 10 Other Kinds of Evolutionary Fuzzy Systems; 10.1 Genetic Fuzzy Neural Networks; 10.2 Genetic Fuzzy Clustering; 10.3 Genetic Fuzzy Decision Trees; Chapter 11 Applications; 11.1 Classification; 11.2 System Modelling; 11.3 Control Systems; 11.4 Robotics; Bibliography; Acronyms; Index |
| Record Nr. | UNINA-9910453555403321 |
| Singapore ; ; River Edge, NJ, : World Scientific, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genetic fuzzy systems : evolutionary tuning and learning of fuzzy knowledge bases / Oscar Cordón ... [et al.]
| Genetic fuzzy systems : evolutionary tuning and learning of fuzzy knowledge bases / Oscar Cordón ... [et al.] |
| Pubbl/distr/stampa | Singapore : World Scientific, 2001 |
| Descrizione fisica | xxv, 462 p. : ill. ; 22 cm |
| Disciplina | 003.7 |
| Altri autori (Persone) | Cordón, Oscarauthor |
| Collana |
Advances in fuzzy systems ; 19
Advances in fuzzy systems - applications and theory ; 19 |
| Soggetto topico |
Fuzzy systems
Genetic algorithms |
| ISBN | 9810240171 |
| Classificazione |
AMS 68T05
AMS 68W05 AMS 68T30 LC QA402.5.G4565 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991000477599707536 |
| Singapore : World Scientific, 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Genetic fuzzy systems [[electronic resource] ] : evolutionary tuning and learning of fuzzy knowledge bases / / Oscar Cordón ... [et al.]
| Genetic fuzzy systems [[electronic resource] ] : evolutionary tuning and learning of fuzzy knowledge bases / / Oscar Cordón ... [et al.] |
| Pubbl/distr/stampa | Singapore ; ; River Edge, NJ, : World Scientific, c2001 |
| Descrizione fisica | 1 online resource (489 p.) |
| Disciplina | 006.31 |
| Altri autori (Persone) | CordónOscar |
| Collana | Advances in fuzzy systems |
| Soggetto topico |
Fuzzy systems
Genetics - Mathematical models |
| ISBN |
9786611956370
1-281-95637-6 981-281-073-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Foreword; Preface; Contents; Chapter 1 Fuzzy Rule-Based Systems; 1.1 Framework: Fuzzy Logic and Fuzzy Systems; 1.2 Mamdani Fuzzy Rule-Based Systems; 1.3 Takagi-Sugeno-Kang Fuzzy Rule-Based Systems; 1.4 Generation of the Fuzzy Rule Set; 1.5 Applying Fuzzy Rule-Based Systems; Chapter 2 Evolutionary Computation; 2.1 Conceptual Foundations of Evolutionary Computation; 2.2 Genetic Algorithms; 2.3 Other Evolutionary Algorithms; Chapter 3 Introduction to Genetic Fuzzy Systems; 3.1 Soft Computing; 3.2 Hybridisation in Soft Computing; 3.3 Integration of Evolutionary Algorithms and Fuzzy Logic
3.4 Genetic Fuzzy SystemsChapter 4 Genetic Tuning Processes; 4.1 Tuning of Fuzzy Rule-Based Systems; 4.2 Genetic Tuning of Scaling Functions; 4.3 Genetic Tuning of Membership Functions of Mamdani Fuzzy Rule-Based Systems; 4.4 Genetic Tuning of TSK Fuzzy Rule Sets; Chapter 5 Learning with Genetic Algorithms; 5.1 Genetic Learning Processes. Introduction; 5.2 The Michigan Approach. Classifier Systems; 5.3 The Pittsburgh Approach; 5.4 The Iterative Rule Learning Approach; Chapter 6 Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach; 6.1 Basic Features of Fuzzy Classifier Systems 6.2 Fuzzy Classifier Systems for Learning Rule Bases6.3 Fuzzy Classifier Systems for Learning Fuzzy Rule Bases; Chapter 7 Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach; 7.1 Coding Rule Bases as Chromosomes; 7.2 Multi-chromosome Genomes (Coding Knowledge Bases); 7.3 Examples; Chapter 8 Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach; 8.1 Coding the Fuzzy Rules; 8.2 Learning Fuzzy Rules under Competition; 8.3 Post-Processing: Refining Rule Bases under Cooperation; 8.4 Inducing Cooperation in the Fuzzy Rule Generation Stage; 8.5 Examples Chapter 9 Other Genetic Fuzzy Rule-Based System Paradigms9.1 Designing Fuzzy Rule-Based Systems with Genetic Progamming; 9.2 Genetic Selection of Fuzzy Rule Sets; 9.3 Learning the Knowledge Base via the Genetic Derivation of the Data Base; 9.4 Other Genetic-Based Machine Learning Approaches; Chapter 10 Other Kinds of Evolutionary Fuzzy Systems; 10.1 Genetic Fuzzy Neural Networks; 10.2 Genetic Fuzzy Clustering; 10.3 Genetic Fuzzy Decision Trees; Chapter 11 Applications; 11.1 Classification; 11.2 System Modelling; 11.3 Control Systems; 11.4 Robotics; Bibliography; Acronyms; Index |
| Record Nr. | UNINA-9910782275803321 |
| Singapore ; ; River Edge, NJ, : World Scientific, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genetic fuzzy systems [[electronic resource] ] : evolutionary tuning and learning of fuzzy knowledge bases / / Oscar Cordón ... [et al.]
| Genetic fuzzy systems [[electronic resource] ] : evolutionary tuning and learning of fuzzy knowledge bases / / Oscar Cordón ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore ; ; River Edge, NJ, : World Scientific, c2001 |
| Descrizione fisica | 1 online resource (489 p.) |
| Disciplina | 006.31 |
| Altri autori (Persone) | CordónOscar |
| Collana | Advances in fuzzy systems |
| Soggetto topico |
Fuzzy systems
Genetics - Mathematical models |
| ISBN |
9786611956370
1-281-95637-6 981-281-073-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Foreword; Preface; Contents; Chapter 1 Fuzzy Rule-Based Systems; 1.1 Framework: Fuzzy Logic and Fuzzy Systems; 1.2 Mamdani Fuzzy Rule-Based Systems; 1.3 Takagi-Sugeno-Kang Fuzzy Rule-Based Systems; 1.4 Generation of the Fuzzy Rule Set; 1.5 Applying Fuzzy Rule-Based Systems; Chapter 2 Evolutionary Computation; 2.1 Conceptual Foundations of Evolutionary Computation; 2.2 Genetic Algorithms; 2.3 Other Evolutionary Algorithms; Chapter 3 Introduction to Genetic Fuzzy Systems; 3.1 Soft Computing; 3.2 Hybridisation in Soft Computing; 3.3 Integration of Evolutionary Algorithms and Fuzzy Logic
3.4 Genetic Fuzzy SystemsChapter 4 Genetic Tuning Processes; 4.1 Tuning of Fuzzy Rule-Based Systems; 4.2 Genetic Tuning of Scaling Functions; 4.3 Genetic Tuning of Membership Functions of Mamdani Fuzzy Rule-Based Systems; 4.4 Genetic Tuning of TSK Fuzzy Rule Sets; Chapter 5 Learning with Genetic Algorithms; 5.1 Genetic Learning Processes. Introduction; 5.2 The Michigan Approach. Classifier Systems; 5.3 The Pittsburgh Approach; 5.4 The Iterative Rule Learning Approach; Chapter 6 Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach; 6.1 Basic Features of Fuzzy Classifier Systems 6.2 Fuzzy Classifier Systems for Learning Rule Bases6.3 Fuzzy Classifier Systems for Learning Fuzzy Rule Bases; Chapter 7 Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach; 7.1 Coding Rule Bases as Chromosomes; 7.2 Multi-chromosome Genomes (Coding Knowledge Bases); 7.3 Examples; Chapter 8 Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach; 8.1 Coding the Fuzzy Rules; 8.2 Learning Fuzzy Rules under Competition; 8.3 Post-Processing: Refining Rule Bases under Cooperation; 8.4 Inducing Cooperation in the Fuzzy Rule Generation Stage; 8.5 Examples Chapter 9 Other Genetic Fuzzy Rule-Based System Paradigms9.1 Designing Fuzzy Rule-Based Systems with Genetic Progamming; 9.2 Genetic Selection of Fuzzy Rule Sets; 9.3 Learning the Knowledge Base via the Genetic Derivation of the Data Base; 9.4 Other Genetic-Based Machine Learning Approaches; Chapter 10 Other Kinds of Evolutionary Fuzzy Systems; 10.1 Genetic Fuzzy Neural Networks; 10.2 Genetic Fuzzy Clustering; 10.3 Genetic Fuzzy Decision Trees; Chapter 11 Applications; 11.1 Classification; 11.2 System Modelling; 11.3 Control Systems; 11.4 Robotics; Bibliography; Acronyms; Index |
| Record Nr. | UNINA-9910825819403321 |
| Singapore ; ; River Edge, NJ, : World Scientific, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
HIS 2009 : Ninth International Conference on Hybrid Intelligent Systems : proceedings,12-14 August 2009, Shenyang, China
| HIS 2009 : Ninth International Conference on Hybrid Intelligent Systems : proceedings,12-14 August 2009, Shenyang, China |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society, 2009 |
| Soggetto topico |
Soft computing
Hybrid computers Neural networks (Computer science) Fuzzy systems Expert systems (Computer science) Artificial intelligence Engineering & Applied Sciences Computer Science |
| ISBN | 1-5090-7119-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996216410103316 |
| [Place of publication not identified], : IEEE Computer Society, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
HIS 2009 : Ninth International Conference on Hybrid Intelligent Systems : proceedings,12-14 August 2009, Shenyang, China
| HIS 2009 : Ninth International Conference on Hybrid Intelligent Systems : proceedings,12-14 August 2009, Shenyang, China |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society, 2009 |
| Soggetto topico |
Soft computing
Hybrid computers Neural networks (Computer science) Fuzzy systems Expert systems (Computer science) Artificial intelligence Engineering & Applied Sciences Computer Science |
| ISBN | 1-5090-7119-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910140022803321 |
| [Place of publication not identified], : IEEE Computer Society, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
HIS'04 : Fourth International Conference on Hybrid Intelligent Systems, proceedings, 5-8 December, 2004, Kitakyushu, Japan
| HIS'04 : Fourth International Conference on Hybrid Intelligent Systems, proceedings, 5-8 December, 2004, Kitakyushu, Japan |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society, 2005 |
| Disciplina | 006.3 |
| Soggetto topico |
Soft computing
Hybrid computers Neural networks (Computer science) Fuzzy systems Expert systems (Computer science) Artificial intelligence Engineering & Applied Sciences Computer Science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996202254803316 |
| [Place of publication not identified], : IEEE Computer Society, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
