Results 41 to 50 of about 377,929 (357)

Multimodal Estimation of Distribution Algorithms [PDF]

open access: yesIEEE Transactions on Cybernetics, 2017
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level.
Qiang Yang 0008   +5 more
openaire   +5 more sources

Estimation of Skill Distributions

open access: yesIEEE Transactions on Information Theory
In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament. These games are played among randomly drawn agents from the population. The agents in our model can be individuals, sports teams, or Wall Street fund managers.
Ali Jadbabaie   +2 more
openaire   +2 more sources

Distribution Estimation for Probabilistic Loops

open access: yes, 2022
We present an algorithmic approach to estimate the value distributions of random variables of probabilistic loops whose statistical moments are (partially) known. Based on these moments, we apply two statistical methods, Maximum Entropy and Gram-Charlier series, to estimate the distributions of the loop's random variables.
Ahmad Karimi   +5 more
openaire   +2 more sources

Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

open access: yesIEEE Access, 2018
In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks.
Jae-Woo Lee   +3 more
doaj   +1 more source

Distributed state estimation for uncertain Markov-type sensor networks with mode-dependent distributed delays

open access: yes, 2011
This the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 John Wiley & Sons, Ltd.In this paper, the distributed state estimation problem is investigated for a class of sensor ...
Zidong Wang   +5 more
core   +1 more source

Decentralized Robust Connectivity Control in Flocking of Multi-Robot Systems

open access: yesIEEE Access, 2020
In this paper, a global connectivity control method for decentralized multi-robot systems is proposed. This method can achieve decentralized connectivity control of multi-robot network under disturbances, which has no effect on the objective flocking ...
Kai Li   +4 more
doaj   +1 more source

Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks

open access: yesIEEE Access, 2020
This paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements.
Qing Shi   +3 more
doaj   +1 more source

On the Estimation of Mixing Distributions

open access: yesThe Annals of Mathematical Statistics, 1966
Let $\mathscr{F} = \{F(x; y), y \varepsilon E\}$ be a family of cumulative distribution functions (cdf's) in the variable $x$ indexed by $y \varepsilon E$, where $E$ is a measurable subset of the real line. Assume that $F(x; y)$ is measurable in $y$ for all $x$.
openaire   +2 more sources

Distributed state estimation in sensor networks with randomly occurring nonlinearities subject to time delays

open access: yes, 2012
This is the post-print version of the Article. The official published version can be accessed from the links below - Copyright @ 2012 ACM.This article is concerned with a new distributed state estimation problem for a class of dynamical systems in sensor
Liu, X, Liang, J, Wang, Z, Shen, B
core   +1 more source

Distributed top-k aggregation queries at large [PDF]

open access: yes, 2009
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network.
Neumann, T.   +17 more
core   +1 more source

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