Proceedings ArticleDOI
Diffusion Least-Mean Squares Over Adaptive Networks
Cassio G. Lopes,Ali H. Sayed +1 more
- Vol. 3, pp 917-920
TLDR
The resulting adaptive networks are robust to node and link failures and present a substantial improvement over the non-cooperative case asserting that cooperation improves estimation performance.Abstract:
Distributed adaptive algorithms are proposed to address the problem of estimation in distributed networks. We extend recent work by relying on static and adaptive diffusion strategies. The resulting adaptive networks are robust to node and link failures and present a substantial improvement over the non-cooperative case asserting that cooperation improves estimation performance. The distributed algorithms are peer-to-peer implementations suitable for networks with general topologies.read more
Citations
More filters
Journal ArticleDOI
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
Cassio G. Lopes,Ali H. Sayed +1 more
TL;DR: Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived and the resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment.
Journal ArticleDOI
Incremental Adaptive Strategies Over Distributed Networks
Cassio G. Lopes,Ali H. Sayed +1 more
TL;DR: An adaptive distributed strategy is developed based on incremental techniques that addresses the problem of linear estimation in a cooperative fashion, in which nodes equipped with local computing abilities derive local estimates and share them with their predefined neighbors.
Journal ArticleDOI
Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks
Jianshu Chen,Ali H. Sayed +1 more
TL;DR: An adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes, which endow networks with adaptation abilities that enable the individual nodes to continue learning even when the cost function changes with time.
Book
Adaptation, Learning, and Optimization Over Networks
TL;DR: The limits of performance of distributed solutions are examined and procedures that help bring forth their potential more fully are discussed and a useful statistical framework is adopted and performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks are derived.
Adaptive Networks
TL;DR: 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.
References
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TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
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Instrumenting the world with wireless sensor networks
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Journal ArticleDOI
Mean-square performance of a convex combination of two adaptive filters
TL;DR: This paper studies the mean-square performance of a convex combination of two transversal filters and shows how the universality of the scheme can be exploited to design filters with improved tracking performance.
Proceedings ArticleDOI
Distributed Recursive Least-Squares Strategies Over Adaptive Networks
Ali H. Sayed,Cassio G. Lopes +1 more
TL;DR: A distributed least-squares estimation strategy is developed by appealing to collaboration techniques that exploit the space-time structure of the data, achieving an exact recursive solution that is fully distributed.
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Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
Cassio G. Lopes,Ali H. Sayed +1 more