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Explainable Uncertain Rule-Based Fuzzy Systems / / by Jerry M. Mendel
| Explainable Uncertain Rule-Based Fuzzy Systems / / by Jerry M. Mendel |
| Autore | Mendel Jerry M |
| Edizione | [3rd ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (598 pages) |
| Disciplina | 511.313 |
| Soggetto topico |
Computational intelligence
Telecommunication Artificial intelligence Neural networks (Computer science) Computational Intelligence Communications Engineering, Networks Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 3-031-35378-1 |
| 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-9910831020703321 |
Mendel Jerry M
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 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 : A Journey into Model-Based Signal Processing / / by Jerry M. Mendel
| Maximum-Likelihood Deconvolution : 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 |
Telecommunication
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-9910957363903321 |
|
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 : Introduction and New Directions, 2nd Edition / / by Jerry M. Mendel
| Uncertain Rule-Based Fuzzy Systems : 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 | ||
| ||