基于随机和多智能体方法的传感器网络控制

Q3 Physics and Astronomy

Cybernetics and Physics Pub Date : 2022-09-30 DOI:10.35470/2226-4116-2022-11-2-94-105

A. Sergeenko, O. Granichin

{"title":"基于随机和多智能体方法的传感器网络控制","authors":"A. Sergeenko, O. Granichin","doi":"10.35470/2226-4116-2022-11-2-94-105","DOIUrl":null,"url":null,"abstract":"In this paper, a development of randomized and multiagent algorithms is presented. The examples and their advantages are discussed. Different combined algorithms which are applicable for the multi-sensor multitarget tracking problem are shown. These algorithms belong to the class of methods used in derivative-free optimization and has proven efficacy in the problems including significant non-statistical uncertainties. The new algorithm which is an Accelerated consensus-based SPSA algorithm is validated through simulation.The main feature of that algorithm, combining the SPSA techniques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.","PeriodicalId":37674,"journal":{"name":"Cybernetics and Physics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sensor network control based on randomized and multi-agent approaches\",\"authors\":\"A. Sergeenko, O. Granichin\",\"doi\":\"10.35470/2226-4116-2022-11-2-94-105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a development of randomized and multiagent algorithms is presented. The examples and their advantages are discussed. Different combined algorithms which are applicable for the multi-sensor multitarget tracking problem are shown. These algorithms belong to the class of methods used in derivative-free optimization and has proven efficacy in the problems including significant non-statistical uncertainties. The new algorithm which is an Accelerated consensus-based SPSA algorithm is validated through simulation.The main feature of that algorithm, combining the SPSA techniques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.\",\"PeriodicalId\":37674,\"journal\":{\"name\":\"Cybernetics and Physics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cybernetics and Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35470/2226-4116-2022-11-2-94-105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Physics and Astronomy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cybernetics and Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35470/2226-4116-2022-11-2-94-105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Physics and Astronomy","Score":null,"Total":0}

引用次数: 0

摘要

本文介绍了一种随机和多智能体算法的发展。讨论了实例及其优点。给出了适用于多传感器多目标跟踪问题的不同组合算法。这些算法属于无导数优化中使用的一类方法，并且已经证明在包括显著的非统计不确定性的问题中是有效的。通过仿真验证了基于加速一致性的SPSA算法。该算法结合了SPSA技术、迭代平均（“本地投票协议”）和Nesterov加速方法，其主要特点是能够在存在完全不确定分布的信号的情况下解决分布式优化问题；唯一的假设是信号的局限性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Sensor network control based on randomized and multi-agent approaches

In this paper, a development of randomized and multiagent algorithms is presented. The examples and their advantages are discussed. Different combined algorithms which are applicable for the multi-sensor multitarget tracking problem are shown. These algorithms belong to the class of methods used in derivative-free optimization and has proven efficacy in the problems including significant non-statistical uncertainties. The new algorithm which is an Accelerated consensus-based SPSA algorithm is validated through simulation.The main feature of that algorithm, combining the SPSA techniques, iterative averaging (“Local Voting Protocol”) and Nesterov Acceleration Method, is the ability to solve distributed optimization problems in the presence of signals with fully uncertain distribution; the only assumption is the signal’s limitation.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Cybernetics and Physics Chemical Engineering-Fluid Flow and Transfer Processes

CiteScore

1.70

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： The scope of the journal includes: -Nonlinear dynamics and control -Complexity and self-organization -Control of oscillations -Control of chaos and bifurcations -Control in thermodynamics -Control of flows and turbulence -Information Physics -Cyber-physical systems -Modeling and identification of physical systems -Quantum information and control -Analysis and control of complex networks -Synchronization of systems and networks -Control of mechanical and micromechanical systems -Dynamics and control of plasma, beams, lasers, nanostructures -Applications of cybernetic methods in chemistry, biology, other natural sciences The papers in cybernetics with physical flavor as well as the papers in physics with cybernetic flavor are welcome. Cybernetics is assumed to include, in addition to control, such areas as estimation, filtering, optimization, identification, information theory, pattern recognition and other related areas.