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Advances in fuzzy clustering and its applications [[electronic resource] /] / edited by J. Valente de Oliveira, W. Pedrycz
Advances in fuzzy clustering and its applications [[electronic resource] /] / edited by J. Valente de Oliveira, W. Pedrycz
Pubbl/distr/stampa Chichester, : Wiley, c2007
Descrizione fisica 1 online resource (456 p.)
Disciplina 006.3
Altri autori (Persone) OliveiraJ. Valente de (José Valente)
PedryczWitold <1953->
Soggetto topico Fuzzy systems
Soft computing
Soggetto genere / forma Electronic books.
ISBN 1-280-90081-4
9786610900817
0-470-06119-7
0-470-06118-9
Classificazione 54.72
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Advances in Fuzzy Clustering and its Applications; Contents; List of Contributors; Foreword; Preface; Part I Fundamentals 1; 1 Fundamentals of Fuzzy Clustering; 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; 2.1 Introduction; 2.2 Object and Relational Data; 2.3 Object Data Clustering Models; 2.4 Relational Clustering; 2.5 Relational Clustering with Non-spherical Prototypes
2.6 Relational Data Interpreted as Object Data2.7 Summary; 2.8 Experiments; 2.9 Conclusions; References; 3 Fuzzy Clustering with Minkowski Distance Functions; 3.1 Introduction; 3.2 Formalization; 3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances; 3.4 The Effects of the Robustness Parameterl; 3.5 Internet Attitudes; 3.6 Conclusions; References; 4 Soft Cluster Ensembles; 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 Visualization5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures; 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; 6.1 Introduction; 6.2 Neighborgram Clustering; 6.3 Interactive Exploration; 6.4 Parallel Universes; 6.5 Discussion; References
Part III Algorithms and Computational Aspects7 Fuzzy Clustering with Participatory Learning and Applications; 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; 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 PerspectivesReferences; 9 Inclusion-based Fuzzy Clustering; 9.1 Introduction; 9.2 Background: Fuzzy Clustering; 9.3 Construction of an Inclusion Index; 9.4 Inclusion-based Fuzzy Clustering; 9.5 Numerical Examples and Illustrations; 9.6 Conclusion; Acknowledgements; Appendix 9A.1; References; 10 Mining Diagnostic Rules Using Fuzzy Clustering; 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 Conclusive Remarks
References
Record Nr. UNINA-9910143587503321
Chichester, : Wiley, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in fuzzy clustering and its applications [[electronic resource] /] / edited by J. Valente de Oliveira, W. Pedrycz
Advances in fuzzy clustering and its applications [[electronic resource] /] / edited by J. Valente de Oliveira, W. Pedrycz
Pubbl/distr/stampa Chichester, : Wiley, c2007
Descrizione fisica 1 online resource (456 p.)
Disciplina 006.3
Altri autori (Persone) OliveiraJ. Valente de (José Valente)
PedryczWitold <1953->
Soggetto topico Fuzzy systems
Soft computing
ISBN 1-280-90081-4
9786610900817
0-470-06119-7
0-470-06118-9
Classificazione 54.72
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Advances in Fuzzy Clustering and its Applications; Contents; List of Contributors; Foreword; Preface; Part I Fundamentals 1; 1 Fundamentals of Fuzzy Clustering; 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; 2.1 Introduction; 2.2 Object and Relational Data; 2.3 Object Data Clustering Models; 2.4 Relational Clustering; 2.5 Relational Clustering with Non-spherical Prototypes
2.6 Relational Data Interpreted as Object Data2.7 Summary; 2.8 Experiments; 2.9 Conclusions; References; 3 Fuzzy Clustering with Minkowski Distance Functions; 3.1 Introduction; 3.2 Formalization; 3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances; 3.4 The Effects of the Robustness Parameterl; 3.5 Internet Attitudes; 3.6 Conclusions; References; 4 Soft Cluster Ensembles; 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 Visualization5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures; 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; 6.1 Introduction; 6.2 Neighborgram Clustering; 6.3 Interactive Exploration; 6.4 Parallel Universes; 6.5 Discussion; References
Part III Algorithms and Computational Aspects7 Fuzzy Clustering with Participatory Learning and Applications; 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; 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 PerspectivesReferences; 9 Inclusion-based Fuzzy Clustering; 9.1 Introduction; 9.2 Background: Fuzzy Clustering; 9.3 Construction of an Inclusion Index; 9.4 Inclusion-based Fuzzy Clustering; 9.5 Numerical Examples and Illustrations; 9.6 Conclusion; Acknowledgements; Appendix 9A.1; References; 10 Mining Diagnostic Rules Using Fuzzy Clustering; 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 Conclusive Remarks
References
Record Nr. UNINA-9910830897803321
Chichester, : Wiley, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in fuzzy clustering and its applications [[electronic resource] /] / edited by J. Valente de Oliveira, W. Pedrycz
Advances in fuzzy clustering and its applications [[electronic resource] /] / edited by J. Valente de Oliveira, W. Pedrycz
Pubbl/distr/stampa Chichester, : Wiley, c2007
Descrizione fisica 1 online resource (456 p.)
Disciplina 006.3
Altri autori (Persone) OliveiraJ. Valente de (José Valente)
PedryczWitold <1953->
Soggetto topico Fuzzy systems
Soft computing
ISBN 1-280-90081-4
9786610900817
0-470-06119-7
0-470-06118-9
Classificazione 54.72
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Advances in Fuzzy Clustering and its Applications; Contents; List of Contributors; Foreword; Preface; Part I Fundamentals 1; 1 Fundamentals of Fuzzy Clustering; 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; 2.1 Introduction; 2.2 Object and Relational Data; 2.3 Object Data Clustering Models; 2.4 Relational Clustering; 2.5 Relational Clustering with Non-spherical Prototypes
2.6 Relational Data Interpreted as Object Data2.7 Summary; 2.8 Experiments; 2.9 Conclusions; References; 3 Fuzzy Clustering with Minkowski Distance Functions; 3.1 Introduction; 3.2 Formalization; 3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances; 3.4 The Effects of the Robustness Parameterl; 3.5 Internet Attitudes; 3.6 Conclusions; References; 4 Soft Cluster Ensembles; 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 Visualization5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures; 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; 6.1 Introduction; 6.2 Neighborgram Clustering; 6.3 Interactive Exploration; 6.4 Parallel Universes; 6.5 Discussion; References
Part III Algorithms and Computational Aspects7 Fuzzy Clustering with Participatory Learning and Applications; 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; 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 PerspectivesReferences; 9 Inclusion-based Fuzzy Clustering; 9.1 Introduction; 9.2 Background: Fuzzy Clustering; 9.3 Construction of an Inclusion Index; 9.4 Inclusion-based Fuzzy Clustering; 9.5 Numerical Examples and Illustrations; 9.6 Conclusion; Acknowledgements; Appendix 9A.1; References; 10 Mining Diagnostic Rules Using Fuzzy Clustering; 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 Conclusive Remarks
References
Record Nr. UNINA-9910841042903321
Chichester, : Wiley, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data engineering [[electronic resource] ] : fuzzy mathematics in systems theory and data analysis / / Olaf Wolkenhauer
Data engineering [[electronic resource] ] : fuzzy mathematics in systems theory and data analysis / / Olaf Wolkenhauer
Autore Wolkenhauer Olaf <1966->
Pubbl/distr/stampa New York, : Wiley, c2001
Descrizione fisica 1 online resource (296 p.)
Disciplina 005.74
511.322
Soggetto topico Database management
Fuzzy systems
System analysis
ISBN 1-280-26475-6
9786610264759
0-470-35673-1
0-471-46410-4
0-471-22434-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; Preface; Acknowledgments; Introduction; 1.1 Overview of the Remaining Chapters; 1.2 Summary of Key Concepts and Ideas; 1.3 Symbols and Notation; 1 System Analysis; 1.1 Uncertainty; 1.2 The Art of Modelling: Linkage; 1.3 Dynamic Systems; 1.4 Example: Coupled Tanks Model; 2 Uncertainty Techniques; 2.1 The Least-Squares Criterion; 2.1.1 Example: Regression Line; 2.1.2 Example: Fourier Series; 2.2 Maximum Likelihood Estimation; 2.2.1 Example: ML-Estimates; 2.2.2 The EM Algorithm; 2.3 Stochastic Processes; 2.3.1 Example: Kalman-Bucy Filtering; 3 Learning from Data: System Identification
3.1 The Probabilistic Perspective3.2 Kernel Density Estimation; 3.3 Basis Function Approximation; 3.4 Example: EM Algorithm; 3.5 Discussion: Modelling and Identification; 4 Propositions as Subsets of the Data Space; 4.1 Hard-c-Means Clustering; 4.2 Least-Squares Functional: Fuzzy Clustering; 4.3 Example: Hard vs. Fuzzy Clustering; 4.4 Orthogonal Transformation; 4.5 Example: Classification; 4.6 Similarity-Based Reasoning; 4.7 The Quotient Induced by Similarity Relations; 5 Fuzzy Systems and Identification; 5.1 Fuzzy Systems Model Structures; 5.2 Identification of Antecedent Fuzzy Sets
5.3 Parameter Identification in the Takagi-Sugeno Model5.4 Example: TS-Modelling and Identification; 5.5 Example: Prediction of a Chaotic Time-Series; 5.6 Discussion; 5.7 Regression Models and Fuzzy Clustering; 5.8 Example: pH Neutralization Process; 6 Random-Set Modelling and Identification; 6.1 Random Variables, Point-Valued Maps; 6.2 Random-Sets, Multi-Valued Maps; 6.3 A Random-Set Approach to System Identification; 6.4 Example 1: Nonlinear AR Process; 6.5 Example 2: Box- Jenkins Gas-Furnace Data; 7 Certain Uncertainty; 7.1 Uncertainty in Systems Analysis
7.2 A Fuzzy Prepositional Calculus7.2.1 Probabilistic Logic; 7.2.2 Classical Two-Valued Logic; 7.2.3 Approximate Reasoning; 8 Fuzzy Inference Engines; 8.1 Composition-Based Inference; 8.2 Individual-Rule-Based Inference; 8.3 Fuzzy Systems as Nonlinear Mappings; 8.4 Example: Comparison of Inference Engines; 9 Fuzzy Classification; 9.1 Equivalence of Fuzzy and Statistical Classifiers; 9.2 Fuzzy Rule-Based Classifier Design; 10 Fuzzy Control; 10.1 PI-Control vs. Fuzzy PI-Control; 10.2 Example 1: First-Order System with Dead-Time; 10.3 Example 2: Coupled Tanks; 11 Fuzzy Mathematics
11.1 The Algebra of Fuzzy Sets11.2 The Extension Principle; 11.3 Fuzzy Rules and Fuzzy Graphs; 11.4 Fuzzy Logic; 11.5 A Bijective Probability - Possibility Transformation; 11.6 Example: Maintenance Decision Making; 11.7 Example: Evaluating Student Performances; 12 Summary; 12.1 System Representations; 12.2 More Philosophical Ideas; 12.2.1 Data Engineering; 13 Appendices; 13.1 Sets, Relations, Mappings; 13.2 Measuring Forecast Accuracy; 13.3 (Hierarchical) Clustering; 13.4 Measure Spaces and Integrals; 13.5 Unbiasedness of Estimators; 13.6 Statistical Reasoning; 13.7 Frequency Analysis; Index
A
Record Nr. UNINA-9910143174903321
Wolkenhauer Olaf <1966->  
New York, : Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Foundations of fuzzy control [[electronic resource]] : a practical approach / / Jan Jantzen
Foundations of fuzzy control [[electronic resource]] : a practical approach / / Jan Jantzen
Autore Jantzen Jan
Edizione [2nd ed.]
Pubbl/distr/stampa Chichester, West Sussex, U.K., : Wiley, c2013
Descrizione fisica 1 online resource (347 p.)
Disciplina 629.8/312
Soggetto topico Automatic control
Fuzzy systems
Fuzzy automata
ISBN 1-118-53559-6
1-118-53560-X
1-118-53558-8
Classificazione TEC009070
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto FOUNDATIONS OF FUZZY CONTROL; Contents; Foreword; Preface to the Second Edition; Preface to the First Edition; 1 Introduction; 1.1 What Is Fuzzy Control?; 1.2 Why Fuzzy Control?; 1.3 Controller Design; 1.4 Introductory Example: Stopping a Car; 1.5 Nonlinear Control Systems; 1.6 Summary; 1.7 The Autopilot Simulator*; 1.8 Notes and References*; 2 Fuzzy Reasoning; 2.1 Fuzzy Sets; 2.1.1 Classical Sets; 2.1.2 Fuzzy Sets; 2.1.3 Universe; 2.1.4 Membership Function; 2.1.5 Possibility; 2.2 Fuzzy Set Operations; 2.2.1 Union, Intersection, and Complement; 2.2.2 Linguistic Variables; 2.2.3 Relations
2.3 Fuzzy If-Then Rules 2.3.1 Several Rules; 2.4 Fuzzy Logic; 2.4.1 Truth-Values; 2.4.2 Classical Connectives; 2.4.3 Fuzzy Connectives; 2.4.4 Triangular Norms; 2.5 Summary; 2.6 Theoretical Fuzzy Logic*; 2.6.1 Tautologies; 2.6.2 Fuzzy Implication; 2.6.3 Rules of Inference; 2.6.4 Generalized Modus Ponens; 2.7 Notes and References*; 3 Fuzzy Control; 3.1 The Rule Based Controller; 3.1.1 Rule Base Block; 3.1.2 Inference Engine Block; 3.2 The Sugeno Controller; 3.3 Autopilot Example: Four Rules; 3.4 Table Based Controller; 3.5 Linear Fuzzy Controller; 3.6 Summary; 3.7 Other Controller Components*
3.7.1 Controller Components 3.8 Other Rule Based Controllers*; 3.8.1 The Mamdani Controller; 3.8.2 The FLS Controller; 3.9 Analytical Simplification of the Inference*; 3.9.1 Four Rules; 3.9.2 Nine Rules; 3.10 Notes and References*; 4 Linear Fuzzy PID Control; 4.1 Fuzzy P Controller; 4.2 Fuzzy PD Controller; 4.3 Fuzzy PD+I Controller; 4.4 Fuzzy Incremental Controller; 4.5 Tuning; 4.5.1 Ziegler-Nichols Tuning; 4.5.2 Hand-Tuning; 4.5.3 Scaling; 4.6 Simulation Example: Third-Order Process; 4.7 Autopilot Example: Stable Equilibrium; 4.7.1 Result; 4.8 Summary
4.9 Derivative Spikes and Integrator Windup*4.9.1 Set point Weighting; 4.9.2 Filtered Derivative; 4.9.3 Anti-Windup; 4.10 PID Loop Shaping*; 4.11 Notes and References*; 5 Nonlinear Fuzzy PID Control; 5.1 Nonlinear Components; 5.2 Phase Plot; 5.3 Four Standard Control Surfaces; 5.4 Fine-Tuning; 5.4.1 Saturation in the Universes; 5.4.2 Limit Cycle; 5.4.3 Quantization; 5.4.4 Noise; 5.5 Example: Unstable Frictionless Vehicle; 5.6 Example: Nonlinear Valve Compensator; 5.7 Example: Motor Actuator with Limits; 5.8 Autopilot Example: Regulating a Mass Load; 5.9 Summary; 5.10 Phase Plane Analysis*
5.10.1 Trajectory in the Phase Plane 5.10.2 Equilibrium Point; 5.10.3 Stability; 5.11 Geometric Interpretation of the PD Controller*; 5.11.1 The Switching Line; 5.11.2 A Rule Base for Switching; 5.12 Notes and References*; 6 The Self-Organizing Controller; 6.1 Model Reference Adaptive Systems; 6.2 The Original SOC; 6.2.1 Adaptation Law; 6.3 A Modified SOC; 6.4 Example with a Long Dead time; 6.4.1 Tuning; 6.4.2 Adaptation; 6.4.3 Performance; 6.5 Tuning and Time Lock; 6.5.1 Tuning of the SOC Parameters; 6.5.2 Time Lock; 6.6 Summary; 6.7 Example: Adaptive Control of a First-Order Process*
6.7.1 The MIT Rule
Record Nr. UNINA-9910141763403321
Jantzen Jan  
Chichester, West Sussex, U.K., : Wiley, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Foundations of fuzzy control [[electronic resource]] : a practical approach / / Jan Jantzen
Foundations of fuzzy control [[electronic resource]] : a practical approach / / Jan Jantzen
Autore Jantzen Jan
Edizione [2nd ed.]
Pubbl/distr/stampa Chichester, West Sussex, U.K., : Wiley, c2013
Descrizione fisica 1 online resource (347 p.)
Disciplina 629.8/312
Soggetto topico Automatic control
Fuzzy systems
Fuzzy automata
ISBN 1-118-53559-6
1-118-53560-X
1-118-53558-8
Classificazione TEC009070
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto FOUNDATIONS OF FUZZY CONTROL; Contents; Foreword; Preface to the Second Edition; Preface to the First Edition; 1 Introduction; 1.1 What Is Fuzzy Control?; 1.2 Why Fuzzy Control?; 1.3 Controller Design; 1.4 Introductory Example: Stopping a Car; 1.5 Nonlinear Control Systems; 1.6 Summary; 1.7 The Autopilot Simulator*; 1.8 Notes and References*; 2 Fuzzy Reasoning; 2.1 Fuzzy Sets; 2.1.1 Classical Sets; 2.1.2 Fuzzy Sets; 2.1.3 Universe; 2.1.4 Membership Function; 2.1.5 Possibility; 2.2 Fuzzy Set Operations; 2.2.1 Union, Intersection, and Complement; 2.2.2 Linguistic Variables; 2.2.3 Relations
2.3 Fuzzy If-Then Rules 2.3.1 Several Rules; 2.4 Fuzzy Logic; 2.4.1 Truth-Values; 2.4.2 Classical Connectives; 2.4.3 Fuzzy Connectives; 2.4.4 Triangular Norms; 2.5 Summary; 2.6 Theoretical Fuzzy Logic*; 2.6.1 Tautologies; 2.6.2 Fuzzy Implication; 2.6.3 Rules of Inference; 2.6.4 Generalized Modus Ponens; 2.7 Notes and References*; 3 Fuzzy Control; 3.1 The Rule Based Controller; 3.1.1 Rule Base Block; 3.1.2 Inference Engine Block; 3.2 The Sugeno Controller; 3.3 Autopilot Example: Four Rules; 3.4 Table Based Controller; 3.5 Linear Fuzzy Controller; 3.6 Summary; 3.7 Other Controller Components*
3.7.1 Controller Components 3.8 Other Rule Based Controllers*; 3.8.1 The Mamdani Controller; 3.8.2 The FLS Controller; 3.9 Analytical Simplification of the Inference*; 3.9.1 Four Rules; 3.9.2 Nine Rules; 3.10 Notes and References*; 4 Linear Fuzzy PID Control; 4.1 Fuzzy P Controller; 4.2 Fuzzy PD Controller; 4.3 Fuzzy PD+I Controller; 4.4 Fuzzy Incremental Controller; 4.5 Tuning; 4.5.1 Ziegler-Nichols Tuning; 4.5.2 Hand-Tuning; 4.5.3 Scaling; 4.6 Simulation Example: Third-Order Process; 4.7 Autopilot Example: Stable Equilibrium; 4.7.1 Result; 4.8 Summary
4.9 Derivative Spikes and Integrator Windup*4.9.1 Set point Weighting; 4.9.2 Filtered Derivative; 4.9.3 Anti-Windup; 4.10 PID Loop Shaping*; 4.11 Notes and References*; 5 Nonlinear Fuzzy PID Control; 5.1 Nonlinear Components; 5.2 Phase Plot; 5.3 Four Standard Control Surfaces; 5.4 Fine-Tuning; 5.4.1 Saturation in the Universes; 5.4.2 Limit Cycle; 5.4.3 Quantization; 5.4.4 Noise; 5.5 Example: Unstable Frictionless Vehicle; 5.6 Example: Nonlinear Valve Compensator; 5.7 Example: Motor Actuator with Limits; 5.8 Autopilot Example: Regulating a Mass Load; 5.9 Summary; 5.10 Phase Plane Analysis*
5.10.1 Trajectory in the Phase Plane 5.10.2 Equilibrium Point; 5.10.3 Stability; 5.11 Geometric Interpretation of the PD Controller*; 5.11.1 The Switching Line; 5.11.2 A Rule Base for Switching; 5.12 Notes and References*; 6 The Self-Organizing Controller; 6.1 Model Reference Adaptive Systems; 6.2 The Original SOC; 6.2.1 Adaptation Law; 6.3 A Modified SOC; 6.4 Example with a Long Dead time; 6.4.1 Tuning; 6.4.2 Adaptation; 6.4.3 Performance; 6.5 Tuning and Time Lock; 6.5.1 Tuning of the SOC Parameters; 6.5.2 Time Lock; 6.6 Summary; 6.7 Example: Adaptive Control of a First-Order Process*
6.7.1 The MIT Rule
Record Nr. UNINA-9910827232703321
Jantzen Jan  
Chichester, West Sussex, U.K., : Wiley, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fuzzy control systems design and analysis [[electronic resource] ] : a linear matrix inequality approach / / Kazuo Tanaka and Hua O. Wang
Fuzzy control systems design and analysis [[electronic resource] ] : a linear matrix inequality approach / / Kazuo Tanaka and Hua O. Wang
Autore Tanaka Kazuo <1962->
Pubbl/distr/stampa New York, : Wiley, c2001
Descrizione fisica 1 online resource (321 p.)
Disciplina 629.832
Altri autori (Persone) WangHua O
Soggetto topico Linear control systems
Fuzzy systems
ISBN 1-280-36765-2
9786610367658
0-470-35221-3
0-471-46522-4
0-471-22459-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto FUZZY CONTROL SYSTEMS DESIGN AND ANALYSIS; CONTENTS; PREFACE; ACRONYMS; 1 INTRODUCTION; 1.1 A Control Engineering Approach to Fuzzy Control; 1.2 Outline of This Book; 2 TAKAGI-SUGENO FUZZY MODEL AND PARALLEL DISTRIBUTED COMPENSATION; 2.1 Takagi-Sugeno Fuzzy Model; 2.2 Construction of Fuzzy Model; 2.2.1 Sector Nonlinearity; 2.2.2 Local Approximation in Fuzzy Partition Spaces; 2.3 Parallel Distributed Compensation; 2.4 A Motivating Example; 2.5 Origin of the LMI-Based Design Approach; 2.5.1 Stable Controller Design via Iterative Procedure
2.5.2 Stable Controller Design via Linear Matrix Inequalities2.6 Application: Inverted Pendulum on a Cart; 2.6.1 Two-Rule Modeling and Control; 2.6.2 Four-Rule Modeling and Control; Bibliography; 3 LMI CONTROL PERFORMANCE CONDITIONS AND DESIGNS; 3.1 Stability Conditions; 3.2 Relaxed Stability Conditions; 3.3 Stable Controller Design; 3.4 Decay Rate; 3.5 Constraints on Control Input and Output; 3.5.1 Constraint on the Control Input; 3.5.2 Constraint on the Output; 3.6 Initial State Independent Condition; 3.7 Disturbance Rejection; 3.8 Design Example: A Simple Mechanical System
3.8.1 Design Case 1: Decay Rate3.8.2 Design Case 2: Decay Rate + Constraint on the Control Input; 3.8.3 Design Case 3: Stability + Constraint on the Control Input; 3.8.4 Design Case 4: Stability + Constraint on the Control Input + Constraint on the Output; References; 4 FUZZY OBSERVER DESIGN; 4.1 Fuzzy Observer; 4.2 Design of Augmented Systems; 4.2.1 Case A; 4.2.2 Case B; 4.3 Design Example; References; 5 ROBUST FUZZY CONTROL; 5.1 Fuzzy Model with Uncertainty; 5.2 Robust Stability Condition; 5.3 Robust Stabilization; References; 6 OPTIMAL FUZZY CONTROL
6.1 Quadratic Performance Function and Stabilization Control6.2 Optimal Fuzzy Controller Design; Appendix to Chapter 6; References; 7 ROBUST-OPTIMAL FUZZY CONTROL; 7.1 Robust-Optimal Fuzzy Control Problem; 7.2 Design Example: TORA; References; 8 TRAJECTORY CONTROL OF A VEHICLE WITH MULTIPLE TRAILERS; 8.1 Fuzzy Modeling of a Vehicle with Triple-Trailers; 8.1.1 Avoidance of Jack-Knife Utilizing Constraint on Output; 8.2 Simulation Results; 8.3 Experimental Study; 8.4 Control of Ten-Trailer Case; References; 9 FUZZY MODELING AND CONTROL OF CHAOTIC SYSTEMS; 9.1 Fuzzy Modeling of Chaotic Systems
9.2 Stabilization9.2.1 Stabilization via Parallel Distributed Compensation; 9.2.2 Cancellation Technique; 9.3 Synchronization; 9.3.1 Case 1; 9.3.2 Case 2; 9.4 Chaotic Model Following Control; References; 10 FUZZY DESCRIPTOR SYSTEMS AND CONTROL; 10.1 Fuzzy Descriptor System; 10.2 Stability Conditions; 10.3 Relaxed Stability Conditions; 10.4 Why Fuzzy Descriptor Systems?; References; 11 NONLINEAR MODEL FOLLOWING CONTROL; 11.1 Introduction; 11.2 Design Concept; 11.2.1 Reference Fuzzy Descriptor System; 11.2.2 Twin-Parallel Distributed Compensations; 11.2.3 The Common B Matrix Case
11.3 Design Examples
Record Nr. UNINA-9910142506203321
Tanaka Kazuo <1962->  
New York, : Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Fuzzy expert systems and fuzzy reasoning [[electronic resource] /] / William Siler, James J. Buckley
Fuzzy expert systems and fuzzy reasoning [[electronic resource] /] / William Siler, James J. Buckley
Autore Siler William
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2005
Descrizione fisica 1 online resource (423 p.)
Disciplina 006.3/3
Altri autori (Persone) BuckleyJames J. <1936->
Soggetto topico Expert systems (Computer science)
Fuzzy systems
ISBN 1-280-25242-1
9786610252428
0-470-35595-6
0-471-69849-0
0-471-69850-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto FUZZY EXPERT SYSTEMS AND FUZZY REASONING; CONTENTS; Preface; 1 Introduction; 1.1 Characteristics of Expert Systems; 1.1.1 Production Systems; 1.1.2 Data-Driven Systems; 1.1.3 Special Features of Fuzzy Systems; 1.1.4 Expert Systems for Fuzzy Control and for Fuzzy Reasoning; 1.2 Neural Nets; 1.3 Symbolic Reasoning; 1.4 Developing a Rule-Based Expert System; 1.5 Fuzzy Rule-Based Systems; 1.6 Problems in Learning How to Construct Fuzzy Expert Systems; 1.7 Tools for Learning How to Construct Fuzzy Expert Systems; 1.8 Auxiliary Reading; 1.9 Summary; 1.10 Questions; 2 Rule-Based Systems: Overview
2.1 Expert Knowledge: Rules and Data; 2.2 Rule Antecedent and Consequent; 2.2.1 Antecedents; 2.2.2 Admissible Data Types; 2.2.3 Consequents; 2.3 Data-Driven Systems; 2.4 Run and Command Modes; 2.4.1 Run Mode: Serial and Parallel Rule Firing; 2.4.2 Checking which Rules are Fireable: The RETE Algorithm; 2.4.3 Serial Rule Firing; 2.4.4 Parallel Rule-Firing; 2.5 Forward and Backward Chaining; 2.6 Program Modularization and Blackboard Systems; 2.7 Handling Uncertainties in an Expert System; 2.8 Summary; 2.9 Questions; 3 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: I; 3.1 Classical Logic
3.1.1 Evaluating A AND B and A OR B3.1.2 The Implication Operator; 3.2 Elementary Fuzzy Logic and Fuzzy Propositions; 3.3 Fuzzy Sets; 3.3.1 Discrete Fuzzy Sets; 3.3.2 Fuzzy Numbers; 3.3.3 Linguistic Variables and Membership Functions; 3.4 Fuzzy Relations; 3.4.1 Matrices of Truth Values; 3.4.2 Relations Between Sets; 3.5 Truth Value of Fuzzy Propositions; 3.5.1 Comparing Single-Valued Data; 3.5.2 Comparing Fuzzy Numbers; 3.6 Fuzzification and Defuzzification; 3.6.1 Fuzzification; 3.6.2 Defuzzification; 3.7 Questions; 4 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: II; 4.1 Introduction
4.2 Algebra of Fuzzy Sets; 4.2.1 T-Norms and t-Conorms: Fuzzy AND and OR Operators; 4.2.2 Correlation Fuzzy Logic; 4.2.3 Combining Fuzzy Numbers; 4.3 Approximate Reasoning; 4.4 Hedges; 4.5 Fuzzy Arithmetic; 4.5.1 Extension Principle; 4.5.2 Alpha-Cut and Interval Arithmetic; 4.5.3 Comparison of Alpha-Cut and Interval Arithmetic Methods; 4.6 Comparisons between Fuzzy Numbers; 4.6.1 Using the Extension Principle; 4.6.2 Alternate Method; 4.7 Fuzzy Propositions; 4.8 Questions; 5 Combining Uncertainties; 5.1 Generalizing AND and OR Operators
5.1.1 Correlation Logic: A Family of Fuzzy Logical Operators that Obeys Excluded Middle and Non-Contradiction Laws5.2 Combining Single Truth Values; 5.3 Combining Fuzzy Numbers and Membership Functions; 5.4 Bayesian Methods; 5.5 The Dempster-Shafer Method; 5.6 Summary; 5.7 Questions; 6 Inference in an Expert System I; 6.1 Overview; 6.2 Types of Fuzzy Inference; 6.3 Nature of Inference in a Fuzzy Expert System; 6.4 Modification and Assignment of Truth Values; 6.4.1 Monotonic Inference; 6.4.2 Non-monotonic Inference; 6.4.3 Downward Monotonic Inference; 6.5 Approximate Reasoning
6.6 Tests of Procedures to Obtain the Truth Value of a Consequent from the Truth Value of Its Antecedent
Record Nr. UNINA-9910146067403321
Siler William  
Hoboken, N.J., : Wiley, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fuzzy expert systems and fuzzy reasoning [[electronic resource] /] / William Siler, James J. Buckley
Fuzzy expert systems and fuzzy reasoning [[electronic resource] /] / William Siler, James J. Buckley
Autore Siler William
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2005
Descrizione fisica 1 online resource (423 p.)
Disciplina 006.3/3
Altri autori (Persone) BuckleyJames J. <1936->
Soggetto topico Expert systems (Computer science)
Fuzzy systems
ISBN 1-280-25242-1
9786610252428
0-470-35595-6
0-471-69849-0
0-471-69850-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto FUZZY EXPERT SYSTEMS AND FUZZY REASONING; CONTENTS; Preface; 1 Introduction; 1.1 Characteristics of Expert Systems; 1.1.1 Production Systems; 1.1.2 Data-Driven Systems; 1.1.3 Special Features of Fuzzy Systems; 1.1.4 Expert Systems for Fuzzy Control and for Fuzzy Reasoning; 1.2 Neural Nets; 1.3 Symbolic Reasoning; 1.4 Developing a Rule-Based Expert System; 1.5 Fuzzy Rule-Based Systems; 1.6 Problems in Learning How to Construct Fuzzy Expert Systems; 1.7 Tools for Learning How to Construct Fuzzy Expert Systems; 1.8 Auxiliary Reading; 1.9 Summary; 1.10 Questions; 2 Rule-Based Systems: Overview
2.1 Expert Knowledge: Rules and Data; 2.2 Rule Antecedent and Consequent; 2.2.1 Antecedents; 2.2.2 Admissible Data Types; 2.2.3 Consequents; 2.3 Data-Driven Systems; 2.4 Run and Command Modes; 2.4.1 Run Mode: Serial and Parallel Rule Firing; 2.4.2 Checking which Rules are Fireable: The RETE Algorithm; 2.4.3 Serial Rule Firing; 2.4.4 Parallel Rule-Firing; 2.5 Forward and Backward Chaining; 2.6 Program Modularization and Blackboard Systems; 2.7 Handling Uncertainties in an Expert System; 2.8 Summary; 2.9 Questions; 3 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: I; 3.1 Classical Logic
3.1.1 Evaluating A AND B and A OR B3.1.2 The Implication Operator; 3.2 Elementary Fuzzy Logic and Fuzzy Propositions; 3.3 Fuzzy Sets; 3.3.1 Discrete Fuzzy Sets; 3.3.2 Fuzzy Numbers; 3.3.3 Linguistic Variables and Membership Functions; 3.4 Fuzzy Relations; 3.4.1 Matrices of Truth Values; 3.4.2 Relations Between Sets; 3.5 Truth Value of Fuzzy Propositions; 3.5.1 Comparing Single-Valued Data; 3.5.2 Comparing Fuzzy Numbers; 3.6 Fuzzification and Defuzzification; 3.6.1 Fuzzification; 3.6.2 Defuzzification; 3.7 Questions; 4 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: II; 4.1 Introduction
4.2 Algebra of Fuzzy Sets; 4.2.1 T-Norms and t-Conorms: Fuzzy AND and OR Operators; 4.2.2 Correlation Fuzzy Logic; 4.2.3 Combining Fuzzy Numbers; 4.3 Approximate Reasoning; 4.4 Hedges; 4.5 Fuzzy Arithmetic; 4.5.1 Extension Principle; 4.5.2 Alpha-Cut and Interval Arithmetic; 4.5.3 Comparison of Alpha-Cut and Interval Arithmetic Methods; 4.6 Comparisons between Fuzzy Numbers; 4.6.1 Using the Extension Principle; 4.6.2 Alternate Method; 4.7 Fuzzy Propositions; 4.8 Questions; 5 Combining Uncertainties; 5.1 Generalizing AND and OR Operators
5.1.1 Correlation Logic: A Family of Fuzzy Logical Operators that Obeys Excluded Middle and Non-Contradiction Laws5.2 Combining Single Truth Values; 5.3 Combining Fuzzy Numbers and Membership Functions; 5.4 Bayesian Methods; 5.5 The Dempster-Shafer Method; 5.6 Summary; 5.7 Questions; 6 Inference in an Expert System I; 6.1 Overview; 6.2 Types of Fuzzy Inference; 6.3 Nature of Inference in a Fuzzy Expert System; 6.4 Modification and Assignment of Truth Values; 6.4.1 Monotonic Inference; 6.4.2 Non-monotonic Inference; 6.4.3 Downward Monotonic Inference; 6.5 Approximate Reasoning
6.6 Tests of Procedures to Obtain the Truth Value of a Consequent from the Truth Value of Its Antecedent
Record Nr. UNINA-9910827978903321
Siler William  
Hoboken, N.J., : Wiley, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fuzzy expert systems and fuzzy reasoning / William Siler, James J. Buckley
Fuzzy expert systems and fuzzy reasoning / William Siler, James J. Buckley
Autore Siler, William
Pubbl/distr/stampa Hoboken, N. J. : Wiley, 2005
Descrizione fisica xvi, 405 p. : ill. ; 24 cm
Disciplina 006.33
Altri autori (Persone) Buckley, James J.
Soggetto topico Expert systems (Computer science)
Fuzzy systems
ISBN 0471388599
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991002290159707536
Siler, William  
Hoboken, N. J. : Wiley, 2005
Materiale a stampa
Lo trovi qui: Univ. del Salento
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