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Complex-valued neural networks : advances and applications / / edited by Akira Hirose
| Complex-valued neural networks : advances and applications / / edited by Akira Hirose |
| Pubbl/distr/stampa | Hoboken [New Jersey] : , : Wiley, , 2013 |
| Descrizione fisica | 1 online resource (310 p.) |
| Disciplina |
006.3/2
006.32 |
| Altri autori (Persone) | HiroseAkira <1963-> |
| Collana | IEEE Press Series on Computational Intelligence |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Computer science) - Industrial applications |
| ISBN |
1-118-59006-6
1-118-59014-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xv -- 1 Application Fields and Fundamental Merits 1 -- Akira Hirose -- 1.1 Introduction 1 -- 1.2 Applications of Complex-Valued Neural Networks 2 -- 1.3 What is a complex number? 5 -- 1.4 Complex numbers in feedforward neural networks 8 -- 1.5 Metric in complex domain 12 -- 1.6 Experiments to elucidate the generalization characteristics 16 -- 1.7 Conclusions 26 -- 2 Neural System Learning on Complex-Valued Manifolds 33 -- Simone Fiori -- 2.1 Introduction 34 -- 2.2 Learning Averages over the Lie Group of Unitary Matrices 35 -- 2.3 Riemannian-Gradient-Based Learning on the Complex Matrix-Hypersphere 41 -- 2.4 Complex ICA Applied to Telecommunications 49 -- 2.5 Conclusion 53 -- 3 N-Dimensional Vector Neuron and Its Application to the N-Bit Parity Problem 59 -- Tohru Nitta -- 3.1 Introduction 59 -- 3.2 Neuron Models with High-Dimensional Parameters 60 -- 3.3 N-Dimensional Vector Neuron 65 -- 3.4 Discussion 69 -- 3.5 Conclusion 70 -- 4 Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus 75 -- Md. Faijul Amin and Kazuyuki Murase -- 4.1 Introduction 76 -- 4.2 Derivatives in Wirtinger Calculus 78 -- 4.3 Complex Gradient 80 -- 4.4 Learning Algorithms for Feedforward CVNNs 82 -- 4.5 Learning Algorithms for Recurrent CVNNs 91 -- 4.6 Conclusion 99 -- 5 Quaternionic Neural Networks for Associative Memories 103 -- Teijiro Isokawa, Haruhiko Nishimura, and Nobuyuki Matsui -- 5.1 Introduction 104 -- 5.2 Quaternionic Algebra 105 -- 5.3 Stability of Quaternionic Neural Networks 108 -- 5.4 Learning Schemes for Embedding Patterns 124 -- 5.5 Conclusion 128 -- 6 Models of Recurrent Clifford Neural Networks and Their Dynamics 133 -- Yasuaki Kuroe -- 6.1 Introduction 134 -- 6.2 Clifford Algebra 134 -- 6.3 Hopfield-Type Neural Networks and Their Energy Functions 137 -- 6.4 Models of Hopfield-Type Clifford Neural Networks 139 -- 6.5 Definition of Energy Functions 140 -- 6.6 Existence Conditions of Energy Functions 142 -- 6.7 Conclusion 149 -- 7 Meta-cognitive Complex-valued Relaxation Network and its Sequential Learning Algorithm 153 -- Ramasamy Savitha, Sundaram Suresh, and Narasimhan Sundararajan.
7.1 Meta-cognition in Machine Learning 154 -- 7.2 Meta-cognition in Complex-valued Neural Networks 156 -- 7.3 Meta-cognitive Fully Complex-valued Relaxation Network 164 -- 7.4 Performance Evaluation of McFCRN: Synthetic Complexvalued Function Approximation Problem 171 -- 7.5 Performance Evaluation of McFCRN: Real-valued Classification Problems 172 -- 7.6 Conclusion 178 -- 8 Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing 185 -- Nikolay V. Manyakov, Igor Aizenberg, Nikolay Chumerin, and Marc M. Van Hulle -- 8.1 Brain-Computer Interface (BCI) 185 -- 8.2 BCI Based on Steady-State Visual Evoked Potentials 188 -- 8.3 EEG Signal Preprocessing 192 -- 8.4 Decoding Based on MLMVN for Phase-Coded SSVEP BCI 196 -- 8.5 System Validation 201 -- 8.6 Discussion 203 -- 9 Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems 209 -- Xia Hong, Sheng Chen and Chris J. Harris -- 9.1 Introduction 210 -- 9.2 Identification and Inverse of Complex-Valued Wiener Systems 211 -- 9.3 Application to Digital Predistorter Design 222 -- 9.4 Conclusions 229 -- 10 Quaternionic Fuzzy Neural Network for View-invariant Color Face Image Recognition 235 -- Wai Kit Wong, Gin Chong Lee, Chu Kiong Loo, Way Soong Lim, and Raymond Lock -- 10.1 Introduction 236 -- 10.2 Face Recognition System 238 -- 10.3 Quaternion-Based View-invariant Color Face Image Recognition 244 -- 10.4 Enrollment Stage and Recognition Stage for Quaternion- Based Color Face Image Correlator 255 -- 10.5 Max-Product Fuzzy Neural Network Classifier 260 -- 10.6 Experimental Results 266 -- 10.7 Conclusion and Future Research Directions 274 -- References 274 -- Index 279. |
| Record Nr. | UNINA-9910141604503321 |
| Hoboken [New Jersey] : , : Wiley, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Complex-valued neural networks : advances and applications / / edited by Akira Hirose
| Complex-valued neural networks : advances and applications / / edited by Akira Hirose |
| Pubbl/distr/stampa | Hoboken [New Jersey] : , : Wiley, , 2013 |
| Descrizione fisica | 1 online resource (310 p.) |
| Disciplina |
006.3/2
006.32 |
| Altri autori (Persone) | HiroseAkira <1963-> |
| Collana | IEEE Press Series on Computational Intelligence |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Computer science) - Industrial applications |
| ISBN |
1-118-59006-6
1-118-59014-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xv -- 1 Application Fields and Fundamental Merits 1 -- Akira Hirose -- 1.1 Introduction 1 -- 1.2 Applications of Complex-Valued Neural Networks 2 -- 1.3 What is a complex number? 5 -- 1.4 Complex numbers in feedforward neural networks 8 -- 1.5 Metric in complex domain 12 -- 1.6 Experiments to elucidate the generalization characteristics 16 -- 1.7 Conclusions 26 -- 2 Neural System Learning on Complex-Valued Manifolds 33 -- Simone Fiori -- 2.1 Introduction 34 -- 2.2 Learning Averages over the Lie Group of Unitary Matrices 35 -- 2.3 Riemannian-Gradient-Based Learning on the Complex Matrix-Hypersphere 41 -- 2.4 Complex ICA Applied to Telecommunications 49 -- 2.5 Conclusion 53 -- 3 N-Dimensional Vector Neuron and Its Application to the N-Bit Parity Problem 59 -- Tohru Nitta -- 3.1 Introduction 59 -- 3.2 Neuron Models with High-Dimensional Parameters 60 -- 3.3 N-Dimensional Vector Neuron 65 -- 3.4 Discussion 69 -- 3.5 Conclusion 70 -- 4 Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus 75 -- Md. Faijul Amin and Kazuyuki Murase -- 4.1 Introduction 76 -- 4.2 Derivatives in Wirtinger Calculus 78 -- 4.3 Complex Gradient 80 -- 4.4 Learning Algorithms for Feedforward CVNNs 82 -- 4.5 Learning Algorithms for Recurrent CVNNs 91 -- 4.6 Conclusion 99 -- 5 Quaternionic Neural Networks for Associative Memories 103 -- Teijiro Isokawa, Haruhiko Nishimura, and Nobuyuki Matsui -- 5.1 Introduction 104 -- 5.2 Quaternionic Algebra 105 -- 5.3 Stability of Quaternionic Neural Networks 108 -- 5.4 Learning Schemes for Embedding Patterns 124 -- 5.5 Conclusion 128 -- 6 Models of Recurrent Clifford Neural Networks and Their Dynamics 133 -- Yasuaki Kuroe -- 6.1 Introduction 134 -- 6.2 Clifford Algebra 134 -- 6.3 Hopfield-Type Neural Networks and Their Energy Functions 137 -- 6.4 Models of Hopfield-Type Clifford Neural Networks 139 -- 6.5 Definition of Energy Functions 140 -- 6.6 Existence Conditions of Energy Functions 142 -- 6.7 Conclusion 149 -- 7 Meta-cognitive Complex-valued Relaxation Network and its Sequential Learning Algorithm 153 -- Ramasamy Savitha, Sundaram Suresh, and Narasimhan Sundararajan.
7.1 Meta-cognition in Machine Learning 154 -- 7.2 Meta-cognition in Complex-valued Neural Networks 156 -- 7.3 Meta-cognitive Fully Complex-valued Relaxation Network 164 -- 7.4 Performance Evaluation of McFCRN: Synthetic Complexvalued Function Approximation Problem 171 -- 7.5 Performance Evaluation of McFCRN: Real-valued Classification Problems 172 -- 7.6 Conclusion 178 -- 8 Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing 185 -- Nikolay V. Manyakov, Igor Aizenberg, Nikolay Chumerin, and Marc M. Van Hulle -- 8.1 Brain-Computer Interface (BCI) 185 -- 8.2 BCI Based on Steady-State Visual Evoked Potentials 188 -- 8.3 EEG Signal Preprocessing 192 -- 8.4 Decoding Based on MLMVN for Phase-Coded SSVEP BCI 196 -- 8.5 System Validation 201 -- 8.6 Discussion 203 -- 9 Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems 209 -- Xia Hong, Sheng Chen and Chris J. Harris -- 9.1 Introduction 210 -- 9.2 Identification and Inverse of Complex-Valued Wiener Systems 211 -- 9.3 Application to Digital Predistorter Design 222 -- 9.4 Conclusions 229 -- 10 Quaternionic Fuzzy Neural Network for View-invariant Color Face Image Recognition 235 -- Wai Kit Wong, Gin Chong Lee, Chu Kiong Loo, Way Soong Lim, and Raymond Lock -- 10.1 Introduction 236 -- 10.2 Face Recognition System 238 -- 10.3 Quaternion-Based View-invariant Color Face Image Recognition 244 -- 10.4 Enrollment Stage and Recognition Stage for Quaternion- Based Color Face Image Correlator 255 -- 10.5 Max-Product Fuzzy Neural Network Classifier 260 -- 10.6 Experimental Results 266 -- 10.7 Conclusion and Future Research Directions 274 -- References 274 -- Index 279. |
| Record Nr. | UNINA-9910830306903321 |
| Hoboken [New Jersey] : , : Wiley, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Complex-valued neural networks : advances and applications / / edited by Akira Hirose
| Complex-valued neural networks : advances and applications / / edited by Akira Hirose |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, Inc., 2013 |
| Descrizione fisica | 1 online resource (310 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) | HiroseAkira |
| Collana | IEEE Press series on computational intelligence |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Computer science) - Industrial applications |
| ISBN |
1-118-59006-6
1-118-59014-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xv -- 1 Application Fields and Fundamental Merits 1 -- Akira Hirose -- 1.1 Introduction 1 -- 1.2 Applications of Complex-Valued Neural Networks 2 -- 1.3 What is a complex number? 5 -- 1.4 Complex numbers in feedforward neural networks 8 -- 1.5 Metric in complex domain 12 -- 1.6 Experiments to elucidate the generalization characteristics 16 -- 1.7 Conclusions 26 -- 2 Neural System Learning on Complex-Valued Manifolds 33 -- Simone Fiori -- 2.1 Introduction 34 -- 2.2 Learning Averages over the Lie Group of Unitary Matrices 35 -- 2.3 Riemannian-Gradient-Based Learning on the Complex Matrix-Hypersphere 41 -- 2.4 Complex ICA Applied to Telecommunications 49 -- 2.5 Conclusion 53 -- 3 N-Dimensional Vector Neuron and Its Application to the N-Bit Parity Problem 59 -- Tohru Nitta -- 3.1 Introduction 59 -- 3.2 Neuron Models with High-Dimensional Parameters 60 -- 3.3 N-Dimensional Vector Neuron 65 -- 3.4 Discussion 69 -- 3.5 Conclusion 70 -- 4 Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus 75 -- Md. Faijul Amin and Kazuyuki Murase -- 4.1 Introduction 76 -- 4.2 Derivatives in Wirtinger Calculus 78 -- 4.3 Complex Gradient 80 -- 4.4 Learning Algorithms for Feedforward CVNNs 82 -- 4.5 Learning Algorithms for Recurrent CVNNs 91 -- 4.6 Conclusion 99 -- 5 Quaternionic Neural Networks for Associative Memories 103 -- Teijiro Isokawa, Haruhiko Nishimura, and Nobuyuki Matsui -- 5.1 Introduction 104 -- 5.2 Quaternionic Algebra 105 -- 5.3 Stability of Quaternionic Neural Networks 108 -- 5.4 Learning Schemes for Embedding Patterns 124 -- 5.5 Conclusion 128 -- 6 Models of Recurrent Clifford Neural Networks and Their Dynamics 133 -- Yasuaki Kuroe -- 6.1 Introduction 134 -- 6.2 Clifford Algebra 134 -- 6.3 Hopfield-Type Neural Networks and Their Energy Functions 137 -- 6.4 Models of Hopfield-Type Clifford Neural Networks 139 -- 6.5 Definition of Energy Functions 140 -- 6.6 Existence Conditions of Energy Functions 142 -- 6.7 Conclusion 149 -- 7 Meta-cognitive Complex-valued Relaxation Network and its Sequential Learning Algorithm 153 -- Ramasamy Savitha, Sundaram Suresh, and Narasimhan Sundararajan.
7.1 Meta-cognition in Machine Learning 154 -- 7.2 Meta-cognition in Complex-valued Neural Networks 156 -- 7.3 Meta-cognitive Fully Complex-valued Relaxation Network 164 -- 7.4 Performance Evaluation of McFCRN: Synthetic Complexvalued Function Approximation Problem 171 -- 7.5 Performance Evaluation of McFCRN: Real-valued Classification Problems 172 -- 7.6 Conclusion 178 -- 8 Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing 185 -- Nikolay V. Manyakov, Igor Aizenberg, Nikolay Chumerin, and Marc M. Van Hulle -- 8.1 Brain-Computer Interface (BCI) 185 -- 8.2 BCI Based on Steady-State Visual Evoked Potentials 188 -- 8.3 EEG Signal Preprocessing 192 -- 8.4 Decoding Based on MLMVN for Phase-Coded SSVEP BCI 196 -- 8.5 System Validation 201 -- 8.6 Discussion 203 -- 9 Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems 209 -- Xia Hong, Sheng Chen and Chris J. Harris -- 9.1 Introduction 210 -- 9.2 Identification and Inverse of Complex-Valued Wiener Systems 211 -- 9.3 Application to Digital Predistorter Design 222 -- 9.4 Conclusions 229 -- 10 Quaternionic Fuzzy Neural Network for View-invariant Color Face Image Recognition 235 -- Wai Kit Wong, Gin Chong Lee, Chu Kiong Loo, Way Soong Lim, and Raymond Lock -- 10.1 Introduction 236 -- 10.2 Face Recognition System 238 -- 10.3 Quaternion-Based View-invariant Color Face Image Recognition 244 -- 10.4 Enrollment Stage and Recognition Stage for Quaternion- Based Color Face Image Correlator 255 -- 10.5 Max-Product Fuzzy Neural Network Classifier 260 -- 10.6 Experimental Results 266 -- 10.7 Conclusion and Future Research Directions 274 -- References 274 -- Index 279. |
| Record Nr. | UNINA-9910876802303321 |
| Hoboken, N.J., : John Wiley & Sons, Inc., 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent computing theories and application . Part I : 18th International Conference, ICIC 2022, Xi'an, China, August 7-11, 2022, proceedings / / editors, De-Shuang Huang [and five others]
| Intelligent computing theories and application . Part I : 18th International Conference, ICIC 2022, Xi'an, China, August 7-11, 2022, proceedings / / editors, De-Shuang Huang [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (858 pages) |
| Disciplina | 006.3 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Computational intelligence
Neural networks (Computer science) - Industrial applications |
| ISBN | 3-031-13870-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Evolutionary Computing and Learning -- Evolutionary Game Analysis of Suppliers Considering Quality Supervision of the Main Manufacturer -- 1 Introduction -- 2 Related Work -- 3 Two-Party Evolutionary Game Between Suppliers -- 3.1 Model Assumptions and Payoff Matrix -- 3.2 Evolutionary Game and Stability Analysis Between Supplier a and Supplier B -- 4 Simulation Analysis -- 5 Conclusion -- References -- Multi-party Evolution Stability Analysis of Electric Vehicles- Microgrid Interaction Mechanism -- 1 Introduction -- 2 Multi-party Evolutionary Game of EV-MG Interaction -- 3 Designing the Game Model -- 3.1 Describing the Game Strategy -- 3.2 Game Solution Analysis -- 3.3 Building the Simulation Model Based on System Dynamics -- 4 Discussion -- 5 Conclusion -- References -- An Efficient Multi-objective Evolutionary Algorithm for a Practical Dynamic Pickup and Delivery Problem -- 1 Introduction -- 2 Preliminary -- 2.1 A Brief Review of DPDPs -- 3 Problem Definition -- 3.1 Objective Functions -- 3.2 Constraints -- 4 The Proposed MOEA/D-ES -- 4.1 Framework -- 4.2 The Crossover Operator -- 4.3 The Local Search Procedure -- 5 Experimental Study -- 5.1 Benchmark Problems and Performance Metrics -- 5.2 The Compared Algorithms and Experimental Settings -- 5.3 The Comparison Experiments on the HW Benchmarks -- 6 Conclusions and Future Work -- References -- An Efficient Evaluation Mechanism for Evolutionary Reinforcement Learning -- 1 Introduction -- 2 Preliminary -- 2.1 Related Works of the Combination of EA and RL -- 3 The Proposed Evaluation Mechanism and E-ERL -- 3.1 The Proposed Evaluation Mechanism -- 3.2 The Proposed E-ERL -- 3.3 Discussions on E-ERL -- 4 Experimental Studies -- 4.1 Benchmark Problems and Performance Metrics.
4.2 The Compared Algorithms and Experimental Settings -- 4.3 Comparisons Between E-ERL and Other Algorithms -- 5 Conclusions and Future Work -- References -- A Mixed-Factor Evolutionary Algorithm for Multi-objective Knapsack Problem -- 1 Introduction -- 2 Background -- 2.1 Multi-objective Evolutionary Algorithm -- 2.2 Multi-objective Knapsack Problem -- 3 Mixed-Factor Evolutionary Algorithm -- 3.1 Definitions -- 3.2 Framework of MFEA -- 3.3 Offspring Creation -- 4 Experiments and Results -- 4.1 Experiment Methodology -- 4.2 Constraints Handling -- 4.3 Experimental Results and Comments -- 5 Conclusion -- References -- NSLS with the Clustering-Based Entropy Selection for Many-Objective Optimization Problems -- 1 Introduction -- 2 Proposed Algorithm NSLS-CE -- 2.1 NSLS -- 2.2 Clustering-Based Entropy Selection -- 2.3 NSLS-CE -- 3 Experimental Result and Discussion -- 3.1 Test Problems -- 3.2 Performance Measures -- 3.3 Parameter Setting -- 3.4 Discussion of Results -- 4 Conclusions -- References -- Tunicate Swarm Algorithm Based Difference Variation Flower Pollination Algorithm -- 1 Introduction -- 2 Flower Pollination Algorithm -- 3 Tunicate Swarm Algorithm Based Differential Variation Flower Pollination Algorithm -- 3.1 Strategy of Simplified TSA -- 3.2 Differential Variation Strategy of Local Pollination -- 3.3 Dynamic Switching Probability Strategy -- 3.4 Procedure of TSA-DVFPA -- 3.5 Time Complexity Analysis of TSA-DVFPA -- 4 Simulation Experiment and Results Analysis -- 4.1 Experimental Parameter Settings -- 4.2 Comparative Analysis of Benchmark Test Function Experiment Results -- 4.3 Ablation Experiments -- 5 Conclusions and Future Works -- References -- A Multi-strategy Improved Fireworks Optimization Algorithm -- 1 Introduction -- 2 Related Work -- 2.1 Fireworks Algorithm -- 2.2 Multi-strategy Fireworks Algorithm. 3 Experimental Simulation and Analysis -- 3.1 Parameter Setting and Sensitivity Analysis -- 3.2 Experimental Results and Analysis -- 3.3 Statistical Test -- 4 Application of MSFWA in Engineering Constrained Optimization Problem -- 5 Concluding Remarks -- Appendix -- References -- A New Fitness-Landscape-Driven Particle Swarm Optimization -- 1 Introduction -- 2 Related Works -- 2.1 Fitness Distance Correlation -- 2.2 Basic Particle Swarm Optimization -- 3 Proposed FLDPSO Algorithm -- 3.1 Fitness-Landscape-Driven Strategy -- 3.2 Variants of PSO -- 3.3 Selection Strategy -- 4 Simulation -- 4.1 Evaluation Function and Parameter Setting -- 4.2 Experimental Results -- 5 Conclusions -- References -- Neighborhood Combination Strategies for Solving the Bi-objective Max-Bisection Problem -- 1 Introduction -- 2 Bi-objective Optimization -- 3 Neighborhood Combination Strategies -- 3.1 One-Flip Move -- 3.2 Two-Flip Move -- 4 Experiments -- 4.1 Parameters Settings -- 4.2 Performance Assessment Protocol -- 4.3 Computational Results -- 5 Conclusions -- References -- Neural Networks -- Rolling Bearing Fault Diagnosis Based on Model Migration -- 1 Introduction -- 2 Introduction of Transfer Learning -- 2.1 Transfer Learning -- 2.2 Methods of Transfer Learning -- 2.3 1D CNN Model and Parameters -- 2.4 Fault Diagnosis Procedure Based on Model Migration -- 3 Bearing Fault Diagnosis Based on Model Migration -- 3.1 Experimental Data -- 3.2 Network Training -- 3.3 Feature Visualization -- 3.4 Performance Comparison -- 4 Conclusion -- References -- Yak Management Platform Based on Neural Network and Path Tracking -- 1 Introduction -- 2 Theoretical Basis -- 2.1 Global Positioning System (GPS) -- 2.2 Neural Network (NN) -- 3 Research Content -- 3.1 Yak Counting Based on Subareas -- 3.2 Individual Analysis Based on Path Tracking -- 4 Function Design and Implementation. 4.1 Functions -- 4.2 Test of Functions -- 5 Conclusion -- References -- Stability Analysis of Hopfield Neural Networks with Conformable Fractional Derivative: M-matrix Method -- 1 Introduction -- 2 Preliminaries -- 3 Main Results -- 4 A Numerical Example -- 5 Conclusions -- References -- Artificial Neural Networks for COVID-19 Forecasting in Mexico: An Empirical Study -- 1 Introduction -- 2 Theoretical Framework -- 2.1 MLP -- 2.2 CNN -- 2.3 LSTM -- 2.4 LSTM-CNN -- 3 Experimental Set Up -- 3.1 Dataset -- 3.2 Free Parameters Specification -- 3.3 Performance of the DL Models -- 4 Results and Discussion -- 5 Conclusion -- References -- Multi-view Robustness-Enhanced Weakly Supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 3.1 Overview -- 3.2 Network Framework -- 3.3 Loss Function -- 4 Experiments -- 4.1 Dataset and Evaluation Metric -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with Other SOTA Methods -- 5 Conclusions -- References -- Rolling Bearing Fault Diagnosis Based on Graph Convolution Neural Network -- 1 Introduction -- 2 Basic Theory -- 2.1 Graph Fourier Transform and Graph Convolution -- 2.2 Rolling Bearing Data Set and Graph Transformation -- 3 Graph Convolution Neural Network -- 3.1 Symbols and Problem Descriptions -- 3.2 Graph Convolution Neural Network Model Selection -- 3.3 Overall Neural Network Structure -- 4 Experimental Verification -- 4.1 Data Sample Division of Experiment 1 -- 4.2 Graph Convolution Neural Network Training of Experiment 1 -- 4.3 Experimental Comparison of Experiment 1 -- 4.4 Data Sample Division of Experiment 2 -- 4.5 Experimental Comparison of Experiment 2 -- 5 Conclusion -- References -- Research on Bearing Fault Feature Extraction Based on Graph Wavelet -- 1 Introduction -- 2 Graph Wavelet Based Bearing Fault Feature Extraction. 2.1 Graph Signal Processing -- 2.2 Graph Wavelet [14] -- 3 Graph Wavelet Based Bearing Fault Feature Extraction -- 3.1 Experimental Data -- 3.2 Analysis of Experimental Results -- 4 Performance Comparison of GWNN and GCN -- 4.1 Experimental Results Analysis -- 4.2 Comparative Analysis of Experimental Results -- 5 Conclusion -- References -- Correntrogram: A Robust Method for Optimal Frequency Band Selection to Bearing Fault Detection -- 1 Introduction -- 2 Correntrogram for Bearing Fault Detection -- 2.1 The Basic Theory of Correntropy -- 2.2 Definition of Spectral L2/L1 Norm and Signal Decomposition -- 2.3 Main Steps of Correntrogram for Bearing Fault Detection -- 3 Simulative Signal Analysis -- 4 Experimental Verification of Bearing Fault Detection -- 5 Conclusions -- References -- Semidefinite Relaxation Algorithm for Source Localization Using Multiple Groups of TDOA Measurements with Distance Constraints -- 1 Introduction -- 2 Proposed Method -- 2.1 Problem Formulation -- 2.2 Maximum Likelihood Estimation of Source Positions -- 2.3 Complexity Analysis -- 2.4 Constrained Cramér-Rao Lower Bound -- 3 Experiment and Analysis -- 4 Conclusion -- References -- Pattern Recognition -- Quasi Fourier Descriptor for Affine Invariant Features -- 1 Introduction -- 2 Preliminary -- 2.1 Affine Transform -- 2.2 Traditional FD -- 3 Affine Invariant Features with QFD -- 3.1 Definition of QFD -- 3.2 Properties of QFD -- 4 Experimental Results -- 4.1 Testing of Invariance -- 4.2 Pattern Classification -- 4.3 Robustness to Noise -- 5 Conclusions -- References -- A New PM2.5 Concentration Predication Study Based on CNN-LSTM Parallel Integration -- 1 Introduction -- 2 A New Parallel Integrated Prediction Method of CNN and LSTM -- 2.1 Parallel Integrated Prediction Model Framework -- 2.2 CNN Model -- 2.3 LSTM Model -- 2.4 LSTM Model -- 3 Experiments and Results. 3.1 Experimental Data. |
| Record Nr. | UNISA-996485668303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Neural networks for instrumentation, measurement, and related industrial applications [[electronic resource] /] / edited by Sergey Ablameyko ... [et al.]
| Neural networks for instrumentation, measurement, and related industrial applications [[electronic resource] /] / edited by Sergey Ablameyko ... [et al.] |
| Pubbl/distr/stampa | Amsterdam ; Burke, VA, : IOS Press |
| Descrizione fisica | 1 online resource (340 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) | AblameykoSergey <1956-> |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Computer science) - Industrial applications |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9786610505760
1-280-50576-1 600-00-0498-2 1-60129-447-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Cover""; ""Title page""; ""Preface""; ""Contents""; ""1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications""; ""1.1 The scientific and application motivations""; ""1.2 The scientific and application objective""; ""1.3 The book organization""; ""1.4 The book topics""; ""1.5 The socio-economical implications""; ""2. The Fundamentals of Measurement Techniques""; ""2.1 The measurement concept""; ""2.2 A big scientific and technical problem""; ""2.3 The uncertainty concept""; ""2.4 Uncertainty: definitions and methods for its determination""
""2.5 How can the results of different measurements be compared?""""2.6 The role of the standard and the traceability concept""; ""2.7 Conclusions""; ""3. Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications""; ""3.1 Introduction to intelligent measurement systems for industrial applications""; ""3.2 Design and implementation of neural-based systems for industrial applications""; ""3.3 Application of neural techniques for intelligent sensors and measurement systems""; ""4. Neural Networks in System Identification""; ""4.1 Introduction"" ""4.2 The main steps of modeling""""4.3 Black box model structures""; ""4.4 Neural networks""; ""4.5 Static neural network architectures""; ""4.6 Dynamic neural architectures""; ""4.7 Model parameter estimation, neural network training""; ""4.8 Model validation""; ""4.9 Why neural networks?""; ""4.10 Modeling of a complex industrial process using neural networks: special difficulties and solutions (case study)""; ""4.11 Conclusions""; ""5. Neural Techniques in Control""; ""5.1 Neural control""; ""5.2 Neural approximations""; ""5.3 Gradient algebra"" ""5.4 Neural modeling of dynamical systems""""5.5 Stabilization""; ""5.6 Tracking""; ""5.7 Optimal control""; ""5.8 Reinforcement learning""; ""5.9 Concluding remarks""; ""6. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing""; ""6.1 Introduction""; ""6.2 Multilayer neural networks""; ""6.3 Dynamical systems""; ""6.4 How can we verify if the behavior is chaotic?""; ""6.5 Embedding parameters""; ""6.6 Lyapunov's exponents""; ""6.7 A neural network approach to compute the Lyapunov's exponents"" ""6.8 Prediction of chaotic processes by using neural networks""""6.9 State space reconstruction""; ""6.10 Conclusion""; ""7. Neural Networks for Image Analysis and Processing in Measurements, Instrumentation and Related Industrial Applications""; ""7.1 Introduction""; ""7.2 Digital imaging systems""; ""7.3 Image system design parameters and modeling""; ""7.4 Multisensor image classification""; ""7.5 Pattern recognition and classification""; ""7.6 Image shape and texture analysis""; ""7.7 Image compression""; ""7.8 Nonlinear neural networks for image compression"" ""7.9 Linear neural networks for image compression"" |
| Record Nr. | UNINA-9910455963603321 |
| Amsterdam ; Burke, VA, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks for instrumentation, measurement, and related industrial applications [[electronic resource] /] / edited by Sergey Ablameyko ... [et al.]
| Neural networks for instrumentation, measurement, and related industrial applications [[electronic resource] /] / edited by Sergey Ablameyko ... [et al.] |
| Pubbl/distr/stampa | Amsterdam ; Burke, VA, : IOS Press |
| Descrizione fisica | 1 online resource (340 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) | AblameykoSergey <1956-> |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Computer science) - Industrial applications |
| ISBN |
9786610505760
1-280-50576-1 600-00-0498-2 1-60129-447-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Cover""; ""Title page""; ""Preface""; ""Contents""; ""1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications""; ""1.1 The scientific and application motivations""; ""1.2 The scientific and application objective""; ""1.3 The book organization""; ""1.4 The book topics""; ""1.5 The socio-economical implications""; ""2. The Fundamentals of Measurement Techniques""; ""2.1 The measurement concept""; ""2.2 A big scientific and technical problem""; ""2.3 The uncertainty concept""; ""2.4 Uncertainty: definitions and methods for its determination""
""2.5 How can the results of different measurements be compared?""""2.6 The role of the standard and the traceability concept""; ""2.7 Conclusions""; ""3. Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications""; ""3.1 Introduction to intelligent measurement systems for industrial applications""; ""3.2 Design and implementation of neural-based systems for industrial applications""; ""3.3 Application of neural techniques for intelligent sensors and measurement systems""; ""4. Neural Networks in System Identification""; ""4.1 Introduction"" ""4.2 The main steps of modeling""""4.3 Black box model structures""; ""4.4 Neural networks""; ""4.5 Static neural network architectures""; ""4.6 Dynamic neural architectures""; ""4.7 Model parameter estimation, neural network training""; ""4.8 Model validation""; ""4.9 Why neural networks?""; ""4.10 Modeling of a complex industrial process using neural networks: special difficulties and solutions (case study)""; ""4.11 Conclusions""; ""5. Neural Techniques in Control""; ""5.1 Neural control""; ""5.2 Neural approximations""; ""5.3 Gradient algebra"" ""5.4 Neural modeling of dynamical systems""""5.5 Stabilization""; ""5.6 Tracking""; ""5.7 Optimal control""; ""5.8 Reinforcement learning""; ""5.9 Concluding remarks""; ""6. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing""; ""6.1 Introduction""; ""6.2 Multilayer neural networks""; ""6.3 Dynamical systems""; ""6.4 How can we verify if the behavior is chaotic?""; ""6.5 Embedding parameters""; ""6.6 Lyapunov's exponents""; ""6.7 A neural network approach to compute the Lyapunov's exponents"" ""6.8 Prediction of chaotic processes by using neural networks""""6.9 State space reconstruction""; ""6.10 Conclusion""; ""7. Neural Networks for Image Analysis and Processing in Measurements, Instrumentation and Related Industrial Applications""; ""7.1 Introduction""; ""7.2 Digital imaging systems""; ""7.3 Image system design parameters and modeling""; ""7.4 Multisensor image classification""; ""7.5 Pattern recognition and classification""; ""7.6 Image shape and texture analysis""; ""7.7 Image compression""; ""7.8 Nonlinear neural networks for image compression"" ""7.9 Linear neural networks for image compression"" |
| Record Nr. | UNINA-9910780291903321 |
| Amsterdam ; Burke, VA, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks for instrumentation, measurement, and related industrial applications / / edited by Sergey Ablameyko ... [et al.]
| Neural networks for instrumentation, measurement, and related industrial applications / / edited by Sergey Ablameyko ... [et al.] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; Burke, VA, : IOS Press |
| Descrizione fisica | 1 online resource (340 p.) |
| Disciplina | 006.32 |
| Altri autori (Persone) | AblameykoSergey <1956-> |
| Collana | NATO science series. Series III, Computer and systems sciences |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Computer science) - Industrial applications |
| ISBN |
9786610505760
1-280-50576-1 600-00-0498-2 1-60129-447-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Cover""; ""Title page""; ""Preface""; ""Contents""; ""1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications""; ""1.1 The scientific and application motivations""; ""1.2 The scientific and application objective""; ""1.3 The book organization""; ""1.4 The book topics""; ""1.5 The socio-economical implications""; ""2. The Fundamentals of Measurement Techniques""; ""2.1 The measurement concept""; ""2.2 A big scientific and technical problem""; ""2.3 The uncertainty concept""; ""2.4 Uncertainty: definitions and methods for its determination""
""2.5 How can the results of different measurements be compared?""""2.6 The role of the standard and the traceability concept""; ""2.7 Conclusions""; ""3. Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications""; ""3.1 Introduction to intelligent measurement systems for industrial applications""; ""3.2 Design and implementation of neural-based systems for industrial applications""; ""3.3 Application of neural techniques for intelligent sensors and measurement systems""; ""4. Neural Networks in System Identification""; ""4.1 Introduction"" ""4.2 The main steps of modeling""""4.3 Black box model structures""; ""4.4 Neural networks""; ""4.5 Static neural network architectures""; ""4.6 Dynamic neural architectures""; ""4.7 Model parameter estimation, neural network training""; ""4.8 Model validation""; ""4.9 Why neural networks?""; ""4.10 Modeling of a complex industrial process using neural networks: special difficulties and solutions (case study)""; ""4.11 Conclusions""; ""5. Neural Techniques in Control""; ""5.1 Neural control""; ""5.2 Neural approximations""; ""5.3 Gradient algebra"" ""5.4 Neural modeling of dynamical systems""""5.5 Stabilization""; ""5.6 Tracking""; ""5.7 Optimal control""; ""5.8 Reinforcement learning""; ""5.9 Concluding remarks""; ""6. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing""; ""6.1 Introduction""; ""6.2 Multilayer neural networks""; ""6.3 Dynamical systems""; ""6.4 How can we verify if the behavior is chaotic?""; ""6.5 Embedding parameters""; ""6.6 Lyapunov's exponents""; ""6.7 A neural network approach to compute the Lyapunov's exponents"" ""6.8 Prediction of chaotic processes by using neural networks""""6.9 State space reconstruction""; ""6.10 Conclusion""; ""7. Neural Networks for Image Analysis and Processing in Measurements, Instrumentation and Related Industrial Applications""; ""7.1 Introduction""; ""7.2 Digital imaging systems""; ""7.3 Image system design parameters and modeling""; ""7.4 Multisensor image classification""; ""7.5 Pattern recognition and classification""; ""7.6 Image shape and texture analysis""; ""7.7 Image compression""; ""7.8 Nonlinear neural networks for image compression"" ""7.9 Linear neural networks for image compression"" |
| Record Nr. | UNINA-9910828434703321 |
| Amsterdam ; Burke, VA, : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of the ... Annual Conference of the IEEE Industrial Electronics Society / / IECON [[electronic resource]]
| Proceedings of the ... Annual Conference of the IEEE Industrial Electronics Society / / IECON [[electronic resource]] |
| Pubbl/distr/stampa | Piscataway, NJ, : IEEE Operations Center |
| Descrizione fisica | 1 online resource |
| Disciplina | 621.381 |
| Soggetto topico |
Industrial electronics
Power electronics Robotics - Industrial applications Neural networks (Computer science) - Industrial applications Computer vision - Industrial applications |
| Soggetto genere / forma | Conference papers and proceedings. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Proceedings, the ... Annual Conference of the IEEE Industrial Electronics Society
IEEE International Conference on Industrial Electronics, Control and Instrumentation IECON |
| Record Nr. | UNISA-996280119503316 |
| Piscataway, NJ, : IEEE Operations Center | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Proceedings of the ... Annual Conference of the IEEE Industrial Electronics Society / / IECON
| Proceedings of the ... Annual Conference of the IEEE Industrial Electronics Society / / IECON |
| Pubbl/distr/stampa | Piscataway, NJ, : IEEE Operations Center |
| Descrizione fisica | 1 online resource |
| Disciplina | 621.381 |
| Soggetto topico |
Industrial electronics
Power electronics Robotics - Industrial applications Neural networks (Computer science) - Industrial applications Computer vision - Industrial applications |
| Soggetto genere / forma | Conference papers and proceedings. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Proceedings, the ... Annual Conference of the IEEE Industrial Electronics Society
IEEE International Conference on Industrial Electronics, Control and Instrumentation IECON |
| Record Nr. | UNINA-9910625197903321 |
| Piscataway, NJ, : IEEE Operations Center | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
SCIMA 2003 : 2003 IEEE International Workshop on Soft Computing in Instrumentation, Measuremment and Related Applications : Brigham Young University, Provo, Utah, USA, 17 May, 2003
| SCIMA 2003 : 2003 IEEE International Workshop on Soft Computing in Instrumentation, Measuremment and Related Applications : Brigham Young University, Provo, Utah, USA, 17 May, 2003 |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE, 2003 |
| Disciplina | 006.3 |
| Soggetto topico |
Soft computing - Industrial applications
Electric power systems - Data processing Neural networks (Computer science) - Industrial applications Fuzzy systems Evolutionary computation Engineering & Applied Sciences Computer Science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996202484703316 |
| [Place of publication not identified], : IEEE, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
