دانلود کتاب Cooperative and graph signal processing : principles and applications

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.  Read more...