Diffusion Adaptation Over Networks Under Imperfect Information Exchange and Non-Stationary Data

Zhao, Xiaochuan; Tu, Sheng-Yuan; Sayed, Ali H.

doi:10.1109/tsp.2012.2192928

Cited by 167 publications

(153 citation statements)

References 53 publications

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“…In addition, the upper bound P u also depends on the value of α used to update the belief in (39). We observe that the larger the value of α, the smaller the values of the error probabilities.…”

Section: Theorem 3 Under Assumptions 3-5 and In The Far-field Regimementioning

confidence: 89%

“…It is clear that the success of the diffusion strategy and decision-making process depends on the reliability of the classification scheme in (33) and (39). In this section, we examine the probability of error for the classification scheme under some simplifying conditions to facilitate the analysis.…”

Section: Performance Of Classification Proceduresmentioning

confidence: 99%

“…Therefore, this approximate analysis suggests that during the initial stages of adaptation, the magnitude of { ĥ k,i } successfully determines that the nodes are in the far-field state and they update the belief using rule (39). When the estimates approach steady-state, the nodes whose observed models are the same as the desired model satisfy the condition z • k − w k,i−1 ≪ 1 and, therefore, they stop updating their belief vectors in view of (75).…”

Section: Lemma 3 Under Assumptions 3-5 It Holds Thatmentioning

confidence: 99%

“…VII of [39], where it is shown how well these strategies track as a function of the level of non-stationarity in the underlying models.…”

mentioning

confidence: 99%

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Distributed Decision-Making Over Adaptive Networks

Sayed

2014

IEEE Trans. Signal Process.

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In distributed processing, agents generally collect data generated by the same underlying unknown model (represented by a vector of parameters) and then solve an estimation or inference task cooperatively.In this paper, we consider the situation in which the data observed by the agents may have risen from two different models. Agents do not know beforehand which model accounts for their data and the data of their neighbors. The objective for the network is for all agents to reach agreement on which model to track and to estimate this model cooperatively. In these situations, where agents are subject to data from unknown different sources, conventional distributed estimation strategies would lead to biased estimates relative to any of the underlying models. We first show how to modify existing strategies to guarantee unbiasedness. We then develop a classification scheme for the agents to identify the models that generated the data, and propose a procedure by which the entire network can be made to converge towards the same model through a collaborative decision-making process. The resulting algorithm is applied to model fish foraging behavior in the presence of two food sources.

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Section: Theorem 3 Under Assumptions 3-5 and In The Far-field Regimementioning

confidence: 89%

Section: Performance Of Classification Proceduresmentioning

confidence: 99%

Section: Lemma 3 Under Assumptions 3-5 It Holds Thatmentioning

confidence: 99%

“…VII of [39], where it is shown how well these strategies track as a function of the level of non-stationarity in the underlying models.…”

mentioning

confidence: 99%

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Distributed Decision-Making Over Adaptive Networks

Sayed

2014

IEEE Trans. Signal Process.

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“…Motivated by [14], we assume a time-varying w 0 of length 6 as follows: . The unknown link is assume to change at time 6000.…”

Section: B) In Non-stationary Environmentmentioning

confidence: 99%

A Robust Diffusion Estimation Algorithm with Self-adjusting Step-size in WSNs

Chen¹,

Ye²,

Shao³

et al. 2017

Preprint

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Abstract:In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

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Transient analysis of multitask learning over adaptive networks with wireless links

Hajiabadi

2023

Adaptive Control & Signal

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SummaryDistributed multitask learning over adaptive networks with non‐ideal links is studied in this paper. The performance of an adaptive multitask network with wireless communication links suffering from flat fading and additive white Gaussian noise is derived through simulation and theoretical analysis. The effect of the wireless link on the learning performance of a multitask network is analyzed. It is shown that this destroying effect is reduced by using local equalizers at each agent along with the intelligent cooperation policy based on the correntropy criterion. Stability condition, steady‐state and transient performances are studied theoretically. Finally, the theoretical results are validated by numerical simulations and computer experiments.

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Diffusion Adaptation Over Networks Under Imperfect Information Exchange and Non-Stationary Data

Cited by 167 publications

References 53 publications

Distributed Decision-Making Over Adaptive Networks

Distributed Decision-Making Over Adaptive Networks

A Robust Diffusion Estimation Algorithm with Self-adjusting Step-size in WSNs

Transient analysis of multitask learning over adaptive networks with wireless links

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