论文标题
扩散策略在网络上的仿真组合
Affine Combination of Diffusion Strategies over Networks
论文作者
论文摘要
扩散适应是针对网络分布式估计和学习的有力策略。这项工作是由结合自适应过滤器的概念的动机，提出了一个组合框架，该框架汇总了多种扩散策略以增强性能的操作。通过将组合系数分配给每个节点，并使用适应机制来最大程度地减少网络误差，我们获得了一种组合扩散策略，该策略从同时根据过度均值平方错误（EMSE）的所有组件策略的最佳特征受益。提供了通用性的分析，以显示仿射组合方案的出色性能，并以均值和均值意义表征其行为。提出了仿真结果，以证明所提出的策略的有效性以及理论发现的准确性。
Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strategies for enhanced performance. By assigning a combination coefficient to each node, and using an adaptation mechanism to minimize the network error, we obtain a combined diffusion strategy that benefits from the best characteristics of all component strategies simultaneously in terms of excess-mean-square error (EMSE). Analyses of the universality are provided to show the superior performance of affine combination scheme and to characterize its behavior in the mean and mean-square sense. Simulation results are presented to demonstrate the effectiveness of the proposed strategies, as well as the accuracy of theoretical findings.
