计算机科学
网络拓扑
适应(眼睛)
趋同(经济学)
实施
分布式计算
人工智能
计算机网络
物理
光学
经济
程序设计语言
经济增长
出处
期刊:Proceedings of the IEEE
[Institute of Electrical and Electronics Engineers]
日期:2014-04-01
卷期号:102 (4): 460-497
被引量:436
标识
DOI:10.1109/jproc.2014.2306253
摘要
This paper surveys recent advances related to adaptation, learning, and optimization over networks. Various distributed strategies are discussed that enable a collection of networked agents to interact locally in response to streaming data and to continually learn and adapt to track drifts in the data and models. Under reasonable technical conditions on the data, the adaptive networks are shown to be mean square stable in the slow adaptation regime, and their mean square error performance and convergence rate are characterized in terms of the network topology and data statistical moments. Classical results for single-agent adaptation and learning are recovered as special cases. The performance results presented in this work are useful in comparing network topologies against each other, and in comparing adaptive networks against centralized or batch implementations. The presentation is complemented with various examples linking together results from various domains.
科研通智能强力驱动
Strongly Powered by AbleSci AI