Diffusion control in multi-agent networks

Chan, Wai Hong Ronald; Wildemeersch, Matthias; Quek, Tony Q. S.

doi:10.1109/cdc.2015.7402872

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Subtract both terms of 18 from d(i). e(i) = α(i)e 1 (i) + [1 − α(i)]e 2 (i) (19) where e(i) = d(i) − y(i), e 1 (i) = d(i) − y 1 (i) and e 2 (i) = d(i) − y 2 (i). Likewise, the overall can be expressed as a convex combination of the apriori error as.…”

Section: Appendix a Steady-state Emse Of Convex Combination Of Two Ad...mentioning

confidence: 99%

“…In decentralized adaptation, multiple nodes (sensors) are used to collect and process the local data from a geographically dispersed environment. Each node, then shares the estimate with its neighbors by using some standard topologies such as incremental [11]- [14], diffusion [15]- [17] and probabilistic diffusion [18], [19], etc. Diffusion mode of cooperation has shown some good results traditionally.…”

Section: Introductionmentioning

confidence: 99%

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Robust Incremental Least Mean Square Algorithm With Dynamic Combiner

et al. 2022

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In distributed wireless networks, the adaptation process depends on the information being shared between various nodes. The global minimum, is therefore, likely to be affected when the information shared between the nodes gets corrupted. This could happen due to several reasons namely link failure, noisy environment and erroneous data etc. In this research, we propose a computationally efficient robust incremental least mean square (RILMS) algorithm to resolve the aforementioned issues. Essentially, a fusion step is introduced in the framework of the incremental least mean square (ILMS). Prior to adaptation at a node, the information shared by the neighbouring node is fused with the temporally preceding information of the node using an efficient combiner. An adaptive fusion strategy is proposed resulting in dynamic weight assignment for the fusion step. Closed form expression for the steady-state excess mean square error (EMSE) is derived and the performance of the proposed algorithm is evaluated for the noisy link environments and compared to the existing algorithms. Extensive experiments show the efficacy of the proposed approach compared to the contemporary methods. The proposed algorithm is found to be robust against the link failure and local node divergence problems. The improved performance of the proposed RILMS algorithm comes with a significant reduction in computational complexity compared to the convex combination based ILMS (CILMS) approach.INDEX TERMS Distributed networks, incremental least mean squares algorithm, decentralized estimation, steady-state analysis, noisy link.

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Section: Appendix a Steady-state Emse Of Convex Combination Of Two Ad...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Incremental Least Mean Square Algorithm With Dynamic Combiner

et al. 2022

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“…Multi-agent network dynamics received ample research interest over the last decade in the context of group coordination [2], distributed algorithms [3], net-$ The material in this paper has been presented in part at the 2015 IEEE Conference on Decision and Control [1].…”

Section: Introductionmentioning

confidence: 99%

“…Email addresses: wildemee@iiasa.ac.at (Matthias Wildemeersch), whrchan@stanford.edu (Wai Hong Ronald Chan), rovenska@iiasa.ac.at (Elena Rovenskaya), tonyquek@sutd.edu.sg (Tony Q. S. Quek) 1 The first two authors have equal contributions to this research.…”

Section: Introductionmentioning

confidence: 99%

Characterization and control of conservative and non-conservative network dynamics

Wildemeersch

Chan

Rovenskaya

et al. 2018

IFAC Journal of Systems and Control

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Diffusion processes are instrumental to describe the movement of a continuous quantity in a network of interacting agents. Here, we present a framework for diffusion in networks and study in particular two classes of agent interactions depending on whether the total network quantity follows a conservation law. Focusing on probabilistic, asymmetric interactions between agents, we define how the dynamics of conservative and non-conservative networks relate to the weighted in-degree and out-degree Laplacians. For uncontrolled networks, we compare the convergence behavior of both types of networks as a function of the eigenvectors of the weighted graph Laplacians. For networks with exogenous controls, we also analyze convergence and provide a method to measure the difference between conservative and non-conservative network dynamics based on the comparison of their respective reachable sets. The presented network control framework enables the comparative study of the dynamic and asymptotic network behavior for conservative and non-conservative networks.

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