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Explainable Uncertain Rule-Based Fuzzy Systems
| Explainable Uncertain Rule-Based Fuzzy Systems |
| Autore | Mendel Jerry M |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (598 pages) |
| Disciplina | 511.313 |
| ISBN | 3-031-35378-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- References -- Contents -- About the Author -- 1: Introduction -- 1.1 What This Book Is About -- 1.1.1 Rules -- 1.1.2 Partitions and Sets -- 1.1.2.1 Crisp Partitions -- 1.1.2.2 First-Order Uncertainty Partitions -- 1.1.2.3 Second-Order Uncertainty Partitions: Uniformly Weighted -- 1.1.2.4 Second-Order Uncertainty Partitions: Nonuniformly Weighted -- 1.1.2.5 Footprint of Uncertainty (FOU) -- 1.1.2.6 Comments -- 1.2 The Structure of a Rule-Based Fuzzy System -- 1.3 A New Direction for Fuzzy Systems -- 1.4 Fundamental Design Requirement -- 1.5 Advisable Design Approaches -- 1.6 Understanding the Potential for Improved Performance -- 1.7 Explainable Fuzzy Systems -- 1.8 An Impressionistic Brief History of Type-1 Fuzzy Sets and Fuzzy Logic -- 1.9 Literature on Type-2 Fuzzy Sets and Fuzzy Systems -- 1.9.1 Early Literature: 1975-1992 -- 1.9.2 Publications that Heavily Influenced the First Edition of This Book -- 1.9.3 Most Cited Articles -- 1.10 Coverage -- 1.11 Applicability Outside of Rule-Based Fuzzy Systems -- 1.12 Computation -- References -- 2: Type-1 Fuzzy Sets and Fuzzy Logic -- 2.1 Crisp Sets -- 2.2 Type-1 Fuzzy Sets and Associated Concepts -- 2.2.1 Lotfi A. Zadeh -- 2.2.2 Type-1 Fuzzy Set Defined -- 2.2.3 Type-1 Fuzzy Numbers -- 2.2.4 Linguistic Variables -- 2.2.5 Returning to Linguistic Labels from Numerical Values of MFs -- 2.3 Set Theoretic Operations for Crisp Sets -- 2.4 Set Theoretic Operations for Type-1 Fuzzy Sets -- 2.5 Crisp Relations and Compositions on the Same Product Space -- 2.6 Fuzzy Relations and Compositions on the Same Product Space -- 2.7 Crisp Relations and Compositions on Different Product Spaces -- 2.8 Fuzzy Relations and Compositions on Different Product Spaces -- 2.9 Hedges -- 2.10 Extension Principle -- 2.11 α-Cuts -- 2.12 Representing Type-1 Fuzzy Sets Using α-Cuts.
2.13 Functions of Type-1 Fuzzy Sets Computed by Using α-Cuts -- 2.14 Multivariable MFs and Cartesian Products -- 2.15 Crisp Logic -- 2.16 From Crisp Logic to Fuzzy Logic -- 2.17 Mamdani (Engineering) Implications -- 2.18 Remarks -- 1.1 Laws That Are Satisfied -- 1.2 Laws That Are Not Satisfied -- Appendix 2: Cardinality and Similarity -- 2.1 Cardinality of Type-1 Fuzzy Sets -- 2.2 Similarity of Type-1 Fuzzy Sets -- References -- 3: Type-1 Fuzzy Systems -- 3.1 Type-1 Fuzzy Systems -- 3.2 Rules -- 3.3 Fuzzifier -- 3.4 Fuzzy Inference Engine -- 3.4.1 General Results -- 3.4.2 Fuzzification and Its Effects on Inference -- 3.4.2.1 Singleton Fuzzifier -- 3.4.2.2 Non-Singleton Fuzzifier -- 3.5 Combining Fired-Rule Output Sets on the Way to Defuzzification -- 3.5.1 Mamdani Fuzzy System: Combining Using Set Theoretic Operations -- 3.5.2 Mamdani Fuzzy System: Combining Using a Weighted Combination -- 3.5.3 Mamdani and TSK Fuzzy Systems: Combining During Defuzzification -- 3.6 Defuzzifier -- 3.6.1 Mamdani Fuzzy System: Centroid Defuzzifier -- 3.6.2 Mamdani Fuzzy System: Height Defuzzifier -- 3.6.3 Mamdani Fuzzy System: COS Defuzzifier -- 3.6.4 TSK Fuzzy System Defuzzifiers -- 3.7 Comprehensive Example -- 3.8 Fuzzy Basis Functions -- 3.9 Sculpting the State Space and the Potential for Improved Performance over a Non-Fuzzy System -- 3.9.1 Course Sculpting of the State Space -- 3.9.2 Fine Sculpting of the State Space -- 3.9.3 Observations -- 3.10 Remarks and Insights -- 3.10.1 Unique Features of Type-1 Fuzzy Systems -- 3.10.2 Layered Architecture Interpretations of a Fuzzy System -- 3.10.3 Functional Equivalence to Other Machine Learning Methods -- 3.10.4 Universal Approximation by Fuzzy Systems -- 3.10.5 Continuity of Fuzzy Systems -- 3.10.6 Rule Explosion and Some Ways to Control It -- 3.10.7 Interpretable and Explainable T1 Fuzzy Systems -- 3.10.7.1 Introduction. 3.10.7.2 On Interpretable -- 3.10.7.3 On Explainable -- 3.10.8 A Top-Down Approach to T1 Fuzzy Systems -- 1.1 Evaluation of Sup-Star Composition for Minimum t-Norm -- 1.2 Evaluation of Sup-Star Composition for Product t-Norm -- 1.3 A Novel Suggestion -- Appendix 2: Constructing Type-1 Rule Partitions -- 2.1 Singleton Fuzzification: T1 First-Order Rule Partitions -- 2.2 Singleton Fuzzification: T1 Second-Order Rule Partitions -- 2.3 Non-Singleton Fuzzification: T1 First-Order Rule Partitions -- 2.4 Non-Singleton Fuzzification: T1 Second-Order Rule Partitions -- 2.5 Rule Crossover Phenomenon -- Appendix 3: Procedure for Determining the Active Rules in a First-Order Rule Partition -- 3.1 First-Order Rule Partition Information Table -- 3.2 Indexing Rules -- 3.3 Determining Rules Associated with x = x′ -- References -- 4: Type-1 Fuzzy Systems: Design Methods and Case Studies -- 4.1 Designing Type-1 Fuzzy Systems -- 4.1.1 Design Choices and Complexity -- 4.1.2 An Interpretation for the Design of a Type-1 Fuzzy System -- 4.1.3 Recapitulation of Mamdani and TSK Fuzzy Systems -- 4.1.4 Number of Design Degrees of Freedom and a Design Principle -- 4.1.5 High-Level Design Statements and Design Approaches -- 4.2 Some Design Methods -- 4.2.1 One-Pass Methods -- 4.2.1.1 Data Assignment Method -- 4.2.1.2 WM Method -- 4.2.2 Clustering Using Fuzzy c-Means (FCM) -- 4.2.3 Least Squares (LS) Method -- 4.2.4 Derivative-Based Methods (Back-Propagation) -- 4.2.5 Derivative-Free Methods -- 4.2.6 Hybrid Design Methods -- 4.2.6.1 Adaptive Network Fuzzy Inference System (ANFIS) -- 4.2.6.2 Structure Identification and Feature Extraction (SIFE) for TSK Systems -- 4.2.7 Remarks -- 4.3 Case Study: Forecasting of Time-Series -- 4.3.1 Mackey-Glass Chaotic Time Series -- 4.3.2 One-Pass Design: Singleton Fuzzification -- 4.3.3 Derivative-Based (BP) Design: Singleton Fuzzification. 4.3.4 A Change in the Measurements -- 4.3.5 One-Pass Design: Non-singleton Fuzzification -- 4.3.6 Derivative-Based (BP) Design: Non-singleton Fuzzification -- 4.3.7 Final Remark -- 4.4 Case Study: Knowledge Mining Using Surveys -- 4.4.1 Methodology for Knowledge Mining -- 4.4.2 Survey Results -- 4.4.3 Determining Type-1 Fuzzy Sets from Survey Results -- 4.4.4 What Does One Do with a Histogram of Responses? -- 4.4.5 Averaging the Responses: Consensus FLAs -- 4.4.6 Preserving All of the Responses -- 4.4.7 On Multiple Indicators -- 4.4.8 How to Use an FLA -- 4.4.9 Connections to the Perceptual Computer -- 4.5 Case Study: Rule-Based Classification of Video Traffic -- 4.5.1 Compressed Video Traffic -- 4.5.2 High-Level Video Classification Problem -- 4.5.3 Selected Features -- 4.5.4 MFs for the Features -- 4.5.5 Rules and Their Parameters -- 4.5.6 Computational Formulas for the RBC -- 4.5.7 Optimization of Rule Design Parameters -- 4.5.8 Testing the FL RBC -- 4.5.9 Results and Conclusions -- 4.6 Case Study: Fuzzy Logic Control -- 4.6.1 Early History of Fuzzy Control -- 4.6.2 What Is a Type-1 Fuzzy Logic Controller (FLC)? -- 4.6.3 Fuzzy PID Control -- 4.6.3.1 Background -- 4.6.3.2 General Structure of Fuzzy PID Controller -- 4.6.3.3 Conventional and Fuzzy PID Controller Design Methods -- 4.6.3.4 Simulation Results (T1-FPID Versus PID) -- 4.7 Case Study: Explainable Type-1 Fuzzy System -- 4.7.1 Computations Common to Both Fuzzy Systems -- 4.7.1.1 Firing Levels for the Active Rules -- 4.7.1.2 Similarities -- 4.7.2 Mamdani with Centroid Defuzzification -- 4.7.2.1 Computation of yc (2.4, 5.4, 9) -- 4.7.2.2 Explaining yc (2.4,5.4,9) -- 4.7.2.3 Quality of Explanation -- 4.7.3 Mamdani with COS Defuzzification -- 4.7.3.1 Computation of yCOS(2.4,5.4,9) -- 4.7.3.2 Explaining yCOS(2.4,5.4,9) -- 4.7.3.3 Observations -- 1.1 Count of MF Parameters. 1.2 T1 MF Constraints -- 1.3 Determine If Satisfying All of the Constraints Is Possible -- 1.4 Constraints Almost Always-Satisfied Parameters (CAASPs) -- 1.5 Comments -- 1.6 Optimizing T1 MF Parameters -- References -- 5: Sources of Uncertainty and Membership Functions -- 5.1 Uncertainties in a Fuzzy System -- 5.1.1 Uncertainty: General Discussions -- 5.1.2 Uncertainties and Sets -- 5.1.3 Uncertainties in a Fuzzy System -- 5.2 Words Mean Different Things to Different People -- 5.2.1 Collecting Word Data by Means of a Survey -- 5.2.2 Making Use of Word Uncertainties -- 5.2.3 Conclusion -- 5.3 Words Must Also Mean Similar Things to Different People -- 5.3.1 Probability-Based Solution of (5.1) -- 5.3.2 Iterative Solution of (5.1)s -- 5.3.3 Example -- 5.4 From Interval Data to a T1 FS -- 5.4.1 Mean and Standard Deviation for Each Data Interval -- 5.4.2 T1 FS Models and Their Mean and Standard Deviation -- 5.4.3 Computation of MF Parameters -- 5.4.4 Choice of T1 MF -- 5.4.5 Ensemble of T1 MFs -- References -- 6: Type-2 Fuzzy Sets Including Word Models -- 6.1 The Concept of a Type-2 Fuzzy Set -- 6.2 Definitions of a General Type-2 Fuzzy Set and Associated Concepts -- 6.3 Definitions of an IT2 FS and Associated Concepts -- 6.4 Examples of Two Popular FOUs -- 6.5 Interval Type-2 Fuzzy Numbers -- 6.6 Different Kinds of T2 FSs: Hierarchy -- 6.7 Mathematical Representations for T2 FSs -- 6.7.1 Vertical Slice Representation -- 6.7.2 Wavy Slice Representations -- 6.7.2.1 General Case -- 6.7.2.2 Covering the FOU -- 6.7.2.3 Minimal Coverings -- 6.7.2.4 Comments -- 6.7.3 Horizontal Slice Representation -- 6.7.4 Modeling Secondary MFs -- 6.8 Representing Non-T2 FSs as T2 FSs -- 6.9 Returning to Linguistic Labels for General T2 FSs -- 6.10 Multivariable Membership Functions -- 6.11 IT2 FS Word Models -- References -- 7: Working with Type-2 Fuzzy Sets. 7.1 Introduction and Guide for the Reader. |
| Record Nr. | UNINA-9910831020703321 |
Mendel Jerry M
|
||
| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Maximum-Likelihood Deconvolution [[electronic resource] ] : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel
| Maximum-Likelihood Deconvolution [[electronic resource] ] : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel |
| Autore | Mendel Jerry M |
| Edizione | [1st ed. 1990.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 1990 |
| Descrizione fisica | 1 online resource (XIV, 227 p.) |
| Disciplina | 621.382 |
| Collana | Signal Processing and Digital Filtering |
| Soggetto topico |
Electrical engineering
Communications Engineering, Networks |
| ISBN | 1-4612-3370-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 - Introduction -- 1.1 Introduction -- 1.2 Our Approach -- 1.3 Likelihood Versus Probability -- 1.4 Maximum-Likelihood Method -- 1.5 Comments -- 2 - Convolutional Model -- 2.1 Introduction -- 2.2 The Seismic Convolutional Model -- 2.3 Input -- 2.4 Channel Model IR (Seismic Wavelet) -- 2.5 Measurement Noise -- 2.6 Other Effects -- 2.7 Mathematical Model -- 2.8 Summary -- 3 - Likelihood -- 3.1 Introduction -- 3.2 Loglikelihood -- 3.3 Likelihood Function -- 3.4 Using Given Information -- 3.5 Message for the Reader -- 3.6 Mathematical Likelihood Functions -- 3.7 Mathematical Loglikelihood Functions -- 3.8 Summary -- 4 - Maximizing Likelihood -- 4.1 Introduction -- 4.2 A Rationale -- 4.3 Block Component Search Algorithms -- 4.4 Mathematical Fact -- 4.5 Separation Principle -- 4.6 Update Random Parameters -- 4.7 Binary Detection -- 4.8 Update Wavelet Parameters -- 4.9 Update Statistical Parameters -- 4.10 Message for the Reader -- 4.11 Summary -- 5 - Properties and Performance -- 5.1 Introduction -- 5.2 Minimum-Variance Deconvolution -- 5.3 Detectors -- 5.4 A Modified Likelihood Function -- 5.5 An Objective Function -- 5.6 Marquardt-Levenberg Algorithm -- 5.7 Convergence -- 5.8 Entropy Interpretation -- 5.9 Summary -- 6 - Examples -- 6.1 Introduction -- 6.2 Some Real Data Examples -- 6.3 Minimum-Variance Deconvolution -- 6.4 Detection -- 6.5 Block Component Method -- 6.6 Backscatter -- 6.7 Noncausal Channel Models -- 6.8 Summary -- 7 - Mathematical Details for Chapter 4 -- 7.1 Introduction -- 7.2 Mathematical Fact -- 7.3 Separation Principle -- 7.4 Minimum-Variance Deconvolution -- 7.5 Threshold Detector -- 7.6 Single Most-Likely Replacement Detector -- 7.7 Single Spike Shift Detector -- 7.8 SSS-SMLR Detector -- 7.9 Marquardt-Levenberg Algorithm -- 7.10 Calculating Gradients -- 7.11 Calculating Second Derivatives -- 7.12 Why vr Cannot be Estimated: Maximization of L or M is an Ill-Posed Problem -- 7.13 An Algorithm for ? -- 8 - Mathematical Details for Chapter 5 -- 8.1 Introduction -- 8.2 MVD Filter Properties -- 8.3 Threshold Detector -- 8.4 Modified Likelihood Function -- 8.5 Separation Principle for P and Derivation of N from P -- 8.6 Why vr Cannot be Estimated: Maximization of P or N is not an Ill-Posed Problem -- 8.7 SMLR1 Detector Based on N -- 8.8 Quadratic Convergence of the Newton-Raphson Algorithm -- 8.9 Wavelet Identifiability -- 8.10 Convergence of Adaptive SMLR Detector -- 9 - Computational Considerations -- 9.1 Introduction -- 9.2 Recursive Processing -- 9.3 Summary -- References. |
| Record Nr. | UNINA-9910480728203321 |
|
Mendel Jerry M
|
||
| New York, NY : , : Springer New York : , : Imprint : Springer, , 1990 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Maximum-Likelihood Deconvolution [[electronic resource] ] : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel
| Maximum-Likelihood Deconvolution [[electronic resource] ] : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel |
| Autore | Mendel Jerry M |
| Edizione | [1st ed. 1990.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 1990 |
| Descrizione fisica | 1 online resource (XIV, 227 p.) |
| Disciplina | 621.382 |
| Altri autori (Persone) | BurrusC. S |
| Collana | Signal Processing and Digital Filtering |
| Soggetto topico |
Electrical engineering
Communications Engineering, Networks |
| ISBN | 1-4612-3370-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 - Introduction -- 1.1 Introduction -- 1.2 Our Approach -- 1.3 Likelihood Versus Probability -- 1.4 Maximum-Likelihood Method -- 1.5 Comments -- 2 - Convolutional Model -- 2.1 Introduction -- 2.2 The Seismic Convolutional Model -- 2.3 Input -- 2.4 Channel Model IR (Seismic Wavelet) -- 2.5 Measurement Noise -- 2.6 Other Effects -- 2.7 Mathematical Model -- 2.8 Summary -- 3 - Likelihood -- 3.1 Introduction -- 3.2 Loglikelihood -- 3.3 Likelihood Function -- 3.4 Using Given Information -- 3.5 Message for the Reader -- 3.6 Mathematical Likelihood Functions -- 3.7 Mathematical Loglikelihood Functions -- 3.8 Summary -- 4 - Maximizing Likelihood -- 4.1 Introduction -- 4.2 A Rationale -- 4.3 Block Component Search Algorithms -- 4.4 Mathematical Fact -- 4.5 Separation Principle -- 4.6 Update Random Parameters -- 4.7 Binary Detection -- 4.8 Update Wavelet Parameters -- 4.9 Update Statistical Parameters -- 4.10 Message for the Reader -- 4.11 Summary -- 5 - Properties and Performance -- 5.1 Introduction -- 5.2 Minimum-Variance Deconvolution -- 5.3 Detectors -- 5.4 A Modified Likelihood Function -- 5.5 An Objective Function -- 5.6 Marquardt-Levenberg Algorithm -- 5.7 Convergence -- 5.8 Entropy Interpretation -- 5.9 Summary -- 6 - Examples -- 6.1 Introduction -- 6.2 Some Real Data Examples -- 6.3 Minimum-Variance Deconvolution -- 6.4 Detection -- 6.5 Block Component Method -- 6.6 Backscatter -- 6.7 Noncausal Channel Models -- 6.8 Summary -- 7 - Mathematical Details for Chapter 4 -- 7.1 Introduction -- 7.2 Mathematical Fact -- 7.3 Separation Principle -- 7.4 Minimum-Variance Deconvolution -- 7.5 Threshold Detector -- 7.6 Single Most-Likely Replacement Detector -- 7.7 Single Spike Shift Detector -- 7.8 SSS-SMLR Detector -- 7.9 Marquardt-Levenberg Algorithm -- 7.10 Calculating Gradients -- 7.11 Calculating Second Derivatives -- 7.12 Why vr Cannot be Estimated: Maximization of L or M is an Ill-Posed Problem -- 7.13 An Algorithm for ? -- 8 - Mathematical Details for Chapter 5 -- 8.1 Introduction -- 8.2 MVD Filter Properties -- 8.3 Threshold Detector -- 8.4 Modified Likelihood Function -- 8.5 Separation Principle for P and Derivation of N from P -- 8.6 Why vr Cannot be Estimated: Maximization of P or N is not an Ill-Posed Problem -- 8.7 SMLR1 Detector Based on N -- 8.8 Quadratic Convergence of the Newton-Raphson Algorithm -- 8.9 Wavelet Identifiability -- 8.10 Convergence of Adaptive SMLR Detector -- 9 - Computational Considerations -- 9.1 Introduction -- 9.2 Recursive Processing -- 9.3 Summary -- References. |
| Record Nr. | UNINA-9910789225803321 |
|
Mendel Jerry M
|
||
| New York, NY : , : Springer New York : , : Imprint : Springer, , 1990 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Maximum-Likelihood Deconvolution [[electronic resource] ] : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel
| Maximum-Likelihood Deconvolution [[electronic resource] ] : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel |
| Autore | Mendel Jerry M |
| Edizione | [1st ed. 1990.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 1990 |
| Descrizione fisica | 1 online resource (XIV, 227 p.) |
| Disciplina | 621.382 |
| Altri autori (Persone) | BurrusC. S |
| Collana | Signal Processing and Digital Filtering |
| Soggetto topico |
Electrical engineering
Communications Engineering, Networks |
| ISBN | 1-4612-3370-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 - Introduction -- 1.1 Introduction -- 1.2 Our Approach -- 1.3 Likelihood Versus Probability -- 1.4 Maximum-Likelihood Method -- 1.5 Comments -- 2 - Convolutional Model -- 2.1 Introduction -- 2.2 The Seismic Convolutional Model -- 2.3 Input -- 2.4 Channel Model IR (Seismic Wavelet) -- 2.5 Measurement Noise -- 2.6 Other Effects -- 2.7 Mathematical Model -- 2.8 Summary -- 3 - Likelihood -- 3.1 Introduction -- 3.2 Loglikelihood -- 3.3 Likelihood Function -- 3.4 Using Given Information -- 3.5 Message for the Reader -- 3.6 Mathematical Likelihood Functions -- 3.7 Mathematical Loglikelihood Functions -- 3.8 Summary -- 4 - Maximizing Likelihood -- 4.1 Introduction -- 4.2 A Rationale -- 4.3 Block Component Search Algorithms -- 4.4 Mathematical Fact -- 4.5 Separation Principle -- 4.6 Update Random Parameters -- 4.7 Binary Detection -- 4.8 Update Wavelet Parameters -- 4.9 Update Statistical Parameters -- 4.10 Message for the Reader -- 4.11 Summary -- 5 - Properties and Performance -- 5.1 Introduction -- 5.2 Minimum-Variance Deconvolution -- 5.3 Detectors -- 5.4 A Modified Likelihood Function -- 5.5 An Objective Function -- 5.6 Marquardt-Levenberg Algorithm -- 5.7 Convergence -- 5.8 Entropy Interpretation -- 5.9 Summary -- 6 - Examples -- 6.1 Introduction -- 6.2 Some Real Data Examples -- 6.3 Minimum-Variance Deconvolution -- 6.4 Detection -- 6.5 Block Component Method -- 6.6 Backscatter -- 6.7 Noncausal Channel Models -- 6.8 Summary -- 7 - Mathematical Details for Chapter 4 -- 7.1 Introduction -- 7.2 Mathematical Fact -- 7.3 Separation Principle -- 7.4 Minimum-Variance Deconvolution -- 7.5 Threshold Detector -- 7.6 Single Most-Likely Replacement Detector -- 7.7 Single Spike Shift Detector -- 7.8 SSS-SMLR Detector -- 7.9 Marquardt-Levenberg Algorithm -- 7.10 Calculating Gradients -- 7.11 Calculating Second Derivatives -- 7.12 Why vr Cannot be Estimated: Maximization of L or M is an Ill-Posed Problem -- 7.13 An Algorithm for ? -- 8 - Mathematical Details for Chapter 5 -- 8.1 Introduction -- 8.2 MVD Filter Properties -- 8.3 Threshold Detector -- 8.4 Modified Likelihood Function -- 8.5 Separation Principle for P and Derivation of N from P -- 8.6 Why vr Cannot be Estimated: Maximization of P or N is not an Ill-Posed Problem -- 8.7 SMLR1 Detector Based on N -- 8.8 Quadratic Convergence of the Newton-Raphson Algorithm -- 8.9 Wavelet Identifiability -- 8.10 Convergence of Adaptive SMLR Detector -- 9 - Computational Considerations -- 9.1 Introduction -- 9.2 Recursive Processing -- 9.3 Summary -- References. |
| Record Nr. | UNINA-9910811750103321 |
|
Mendel Jerry M
|
||
| New York, NY : , : Springer New York : , : Imprint : Springer, , 1990 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Uncertain Rule-Based Fuzzy Systems [[electronic resource] ] : Introduction and New Directions, 2nd Edition / / by Jerry M. Mendel
| Uncertain Rule-Based Fuzzy Systems [[electronic resource] ] : Introduction and New Directions, 2nd Edition / / by Jerry M. Mendel |
| Autore | Mendel Jerry M |
| Edizione | [2nd ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XXII, 684 p. 215 illus., 192 illus. in color.) |
| Disciplina | 511.313 |
| Soggetto topico |
Electrical engineering
Computational intelligence Artificial intelligence Neural networks (Computer science) Communications Engineering, Networks Computational Intelligence Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 3-319-51370-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part 1: Type-1 Fuzzy Sets and Systems -- Short Primers on Type-1 Fuzzy Sets and Fuzzy Logic -- Type-1 Fuzzy Logic Systems -- Part 2: Type-2 Fuzzy Sets -- Sources of Uncertainty -- Type-2 Fuzzy Sets -- Operations on and Properties OF Type-2 Fuzzy Sets -- Type-2 Relations and Compositions -- Centroid of a Type-2 Fuzzy Set: Type-Reduction -- Part 3: Type-2 Fuzzy Logic Systems -- Mamdani Interval Type-2 Fuzzy Logic Systems (IT2 FLSS) -- TSK Interval Type-2 Fuzzy Logic Systems -- General Type-2 Fuzzy Logic Systems (GT2 FLSS) -- Conclusion. |
| Record Nr. | UNINA-9910254328903321 |
|
Mendel Jerry M
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||