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Cooperative and Graph Signal Processing : Principles and Applications /
"Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press, an imprint of Elsevier,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Cooperative and Graph Signal Processing: Principles and Applications; Copyright; Contents; Contributors; Preface; Part 1: Basics of Inference Over Networks; Chapter 1: Asynchronous Adaptive Networks; 1.1 Introduction; 1.1.1 Asynchronous Behavior; 1.1.2 Organization of the Chapter; 1.2 Single-Agent Adaptation and Learning; 1.2.1 Risk and Loss Functions; 1.2.2 Conditions on Cost Function; 1.2.3 Stochastic-Gradient Approximation; 1.2.4 Conditions on Gradient Noise Process; 1.2.5 Random Updates; 1.2.6 Mean-Square-Error Stability; 1.2.7 Mean-Square-Error Performance.
- 1.3 Centralized Adaptation and Learning1.3.1 Noncooperative MSE Processing; 1.3.2 Centralized MSE Processing; 1.3.3 Stochastic-Gradient Centralized Solution; 1.3.4 Performance of Centralized Solution; 1.3.5 Comparison With Noncooperative Processing; 1.4 Synchronous Multiagent Adaptation and Learning; 1.4.1 Strongly Connected Networks; 1.4.2 Distributed Optimization; 1.4.3 Synchronous Consensus Strategy; 1.4.4 Synchronous Diffusion Strategies; 1.5 Asynchronous Multiagent Adaptation and Learning; 1.5.1 Asynchronous Model; 1.5.2 Mean Graph; 1.5.3 Random Combination Policy.
- 1.5.4 Perron Vectors1.6 Asynchronous Network Performance; 1.6.1 MSD Performance; 1.7 Network Stability and Performance; 1.7.1 MSE Networks; 1.7.2 Diffusion Networks; 1.8 Concluding Remarks; References; Chapter 2: Estimation and Detection Over Adaptive Networks; 2.1 Introduction; 2.2 Inference Over Networks; 2.2.1 Canonical Inference Problems; 2.2.2 Distributed Inference Problem; Architectures with fusion center; Fully flat architectures; 2.2.3 Inference Over Adaptive Networks; 2.3 Diffusion Implementations; 2.4 Distributed Adaptive Estimation (DAE).
- 2.4.1 Constructing the Distributed Adaptive Estimation Algorithm2.4.2 Mean-Square-Error Performance; 2.4.3 Useful Comparisons; 2.4.4 DAE at Work; 2.5 Distributed Adaptive Detection (DAD); 2.5.1 Constructing the Distributed Adaptive Detection Algorithm; 2.5.2 Detection Performance; 2.5.3 Weak Law of Small Step-Sizes; 2.5.4 Asymptotic Normality; 2.5.5 Large Deviations; 2.5.6 Refined Large Deviations Analysis: Exact Asymptotics; 2.5.7 DAD at Work; 2.6 Universal Scaling Laws: Estimation Versus Detection; Appendix; A.1 Procedure to Evaluate Eq. (2.69); References.
- Chapter 3: Multitask Learning Over Adaptive Networks With Grouping Strategies3.1 Introduction; 3.2 Network Model and Diffusion LMS; 3.2.1 Network Model; 3.2.2 A Brief Review of Diffusion LMS; 3.3 Group Diffusion LMS; 3.3.1 Motivation; 3.3.2 Group Diffusion LMS Algorithm; 3.3.3 Network Behavior; Mean weight behavior analysis; Mean-square error behavior analysis; 3.4 Grouping Strategies; 3.4.1 Fixed Grouping Strategy; 3.4.2 Adaptive Grouping Strategy; 3.4.3 Adaptive Combination Strategy; 3.5 Simulations; 3.5.1 Model Validation; 3.5.2 Performance of the Adaptive Grouping Strategy.