This paper studies system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. Using the maximum likelihood criterion, we propose a recursive identification ...
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This paper studies system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. Using the maximum likelihood criterion, we propose a recursive identification algorithm, which we show to be strongly consistent and asymptotically normal. We also propose a simple adaptive quantization scheme, which asymptotically achieves the minimum parameter estimation error covariance. The joint effect of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified. The theoretical results are verified by simulations. (C) 2012 Elsevier Ltd. All rights reserved.
In this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value;however,...
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In this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value;however, the asymptotic convergence of these algorithms poses a challenge in practical settings where the response from agents is required in finite time. To this end, we propose a maximum-minimum protocol that propagates the global maximum and minimum values of agent states (while running the consensus algorithm) in the network. This article focuses on establishing that the global maximum and minimum values are strictly monotonic even for a dynamic topology, and they can be used to distributively ascertain the closeness to convergence in finite time. We rigorously show that each node can have access to the global maximum and minimum by running the proposed maximum-minimum protocol to realize a finite-time stopping criterion for the otherwise asymptotic consensus algorithm. The practical utility of the algorithm is illustrated through experiments where each agent is instantiated by a NodeJS *** server.
We establish finite time termination algorithms for consensus algorithms based on geometric properties that yield finite-time guarantees, suited for use in high dimension and in the absence of a central authority. The...
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We establish finite time termination algorithms for consensus algorithms based on geometric properties that yield finite-time guarantees, suited for use in high dimension and in the absence of a central authority. These pursuits motivate a new peer to peer convex hull algorithm, which is utilized for one stopping algorithm. Further an alternative lightweight norm based stopping criteria is also developed. The practical utility of the algorithm is illustrated through MATLAB simulations.
We present insights into the geometry of the ratio consensus algorithm that lead to finite time distributed stopping criteria for the algorithm in higher dimension. In particular we show that the polytopes of network ...
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We present insights into the geometry of the ratio consensus algorithm that lead to finite time distributed stopping criteria for the algorithm in higher dimension. In particular we show that the polytopes of network states indexed by time form a nested sequence. This monotonicity allows the construction of a distributed algorithm that terminates in finite time when applied to consensus problems in any dimension and guarantees the convergence of the consensus algorithm in norm, within any given tolerance. The practical utility of the algorithm is illustrated through MATLAB simulations. Copyright (C) 2020 The Authors.
We consider a distributed estimation method in a setting with heterogeneous streams of correlated data distributed across nodes in a network. In the considered approach, linear models are estimated locally (i.e., with...
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We consider a distributed estimation method in a setting with heterogeneous streams of correlated data distributed across nodes in a network. In the considered approach, linear models are estimated locally (i.e., with only local data) subject to a network regularization term that penalizes a local model that differs from neighboring models. We analyze computation dynamics (associated with stochastic gradient updates) and information exchange (associated with exchanging current models with neighboring nodes). We provide a finite-time characterization of convergence of the weighted ensemble average estimate and compare this result to federated learning, an alternative approach to estimation wherein a single model is updated by locally generated gradient updates. This comparison highlights the trade-off between speed vs precision: while model updates take place at a faster rate in federated learning, the proposed networked approach to estimation enables the identification of models with higher precision. We illustrate the method's general applicability in two examples: estimating a Markov random field using wireless sensor networks and modeling prey escape behavior of flocking birds based on a publicly available dataset. (C) 2021 Elsevier Ltd. All rights reserved.
We present insights into the geometry of the ratio consensus algorithm that lead to finite time distributed stopping criteria for the algorithm in higher dimension. In particular we show that the polytopes of network ...
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We present insights into the geometry of the ratio consensus algorithm that lead to finite time distributed stopping criteria for the algorithm in higher dimension. In particular we show that the polytopes of network states indexed by time form a nested sequence. This monotonicity allows the construction of a distributed algorithm that terminates in finite time when applied to consensus problems in any dimension and guarantees the convergence of the consensus algorithm in norm, within any given tolerance. The practical utility of the algorithm is illustrated through MATLAB simulations.
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