Combination weights for diffusion strategies with imperfect information exchange

Zhao, Xiaochuan; Sayed, Ali H.

doi:10.1109/icc.2012.6364339

Cited by 9 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although we can continue our analysis by studying this general case in which the vectors z i do not have zero-mean (see [62,63]), we shall nevertheless limit our discussion in the sequel to the case in which there is no noise during the exchange of the regression data, i.e., we henceforth assume that:…”

Section: Error Recursionmentioning

confidence: 99%

Diffusion Adaptation Over Networks

Sayed

2014

Academic Press Library in Signal Processing

Self Cite

364

533

View full text Add to dashboard Cite

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed optimization, estimation, and inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to streaming data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative agents. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation. Comparison Conditions MSD network atc ≤ MSD network cta A doubly stochastic, C right stochastic. MSD network cta,C =I ≤ MSD network cta,C=I C T RvC ≤ Rv, C doubly stochastic, R u,k = Ru, µ k = µ. MSD network cta,C=I ≤ MSD network cta,C =I C T RvC ≥ Rv, C doubly stochastic, R u,k = Ru, µ k = µ. MSD network atc,C =I ≤ MSD network atc,C=I C T RvC ≤ Rv, C doubly stochastic, R u,k = Ru, µ k = µ. MSD network atc,C=I ≤ MSD network atc,C =I C T RvC ≥ Rv, C doubly stochastic, R u,k = Ru, µ k = µ. MSD network atc ≤ MSD network cta ≤ MSD network lms {A, C} doubly stochastic, R u,k = Ru, µ k = µ.

show abstract

Section: Error Recursionmentioning

confidence: 99%

Diffusion Adaptation Over Networks

Sayed

2014

Academic Press Library in Signal Processing

Self Cite

364

533

View full text Add to dashboard Cite

show abstract

“…where ℛ υ p collects all covariance matrices ℛ υ, rk p corresponding to the evaluated price data noise [v k (i)] k ∈ K throughout the diffusion network. It is shown in [44,46] that minimising the upper bound of the network MSD and EMSE for Algorithm 2 over left-stochastic combination matrices D leads to the following relative variance rule:…”

Section: Robustness Analysismentioning

confidence: 99%

“…We showed that the mean‐square stability of Algorithm 2 is independent of the combination weights and topology. However, is it shown in [44] that link noise and imperfect information exchange over the regression data (price signal

scriptP

Section: Diffusion Strategymentioning

confidence: 99%

“…However, is it shown in [44] that link noise and imperfect information exchange over the regression data (price signal

scriptP

) modifies the dynamics of the network evolution, and leads to biased estimates in steady‐state, deteriorating the performance of the proposed solution. The analysis also reveals how the network mean‐square performance is affected by modifying weight matrices [44]. So, there is an urgent need to devise a proper robust mechanism for scaling/modifying combination weights adaptively in order to reduce the mean‐square error measurement of the system in the presence of disturbances and asynchrony in Algorithm 2.…”

Section: Diffusion Strategymentioning

confidence: 99%

See 1 more Smart Citation

Demand‐side management for smart grid via diffusion adaptation

et al. 2020

View full text Add to dashboard Cite

This study presents a novel fully distributed and cooperative demand side management framework based on adaptive diffusion strategy. In this approach, each customer autonomously and without any need for the global information, minimises his incommodity function. The proposed framework has ability to track drifts resulting from the changes in the customer preferences and conditions or any rapidly changing price parameter coming from the wholesale market. In this scenario, the customers aim at maximising their individual utility functions; while the utility company aims at minimising the smart grid total payment (i.e. maximisation of the social welfare). The authors show that there is no need for the utility company to participate in the scheduling program for maximising social welfare. This measurement is maximised adaptively when the customers minimise their incommodity. Moreover, the authors provide a detailed analysis of the robustness of the proposed strategy in the presence of imperfect communication/computation conditions. Numerical results show that the proposed framework performs well, is scale free, and can achieve lower peak-to-average ratio of the total energy demand compared with that achieved by the game theoretical methods.

show abstract

“…The works [5]- [7], [9]- [12] show that noisy exchanges result in performance degradation of distributed strategies. Then, in order to counter the effect of such degradation, resilient distributed schemes are developed in [5]- [7], [9], [12]- [17].…”

Section: Introductionmentioning

confidence: 99%

Resilient Multitask Distributed Adaptation Over Networks With Noisy Exchanges

Wang

Tay

et al. 2020

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

View full text Add to dashboard Cite

We develop a resilient distributed strategy over multitask networks, where individual tasks are linearly related within each neighborhood, and information exchanges between neighboring agents are noisy. In the proposed strategy, each agent follows an adapt-then-project procedure to iteratively update its local estimate. In particular, weighted projection operators are utilized in the projection step in order to attenuate the negative effect of noisy exchanges on the cooperative inference performance. We motivate a strategy for computing the weights in a distributed and adaptive manner. Simulation results demonstrate that the proposed scheme shows good resilience against noise in the information exchange between agents.

show abstract

Combination weights for diffusion strategies with imperfect information exchange

Cited by 9 publications

References 15 publications

Diffusion Adaptation Over Networks

Diffusion Adaptation Over Networks

Demand‐side management for smart grid via diffusion adaptation

Resilient Multitask Distributed Adaptation Over Networks With Noisy Exchanges

Contact Info

Product

Resources

About