抽样问题的生物启发多用途方法

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Computation Pub Date : 2023-12-14 DOI:10.3390/computation11120254

Anton Tolstikhin

{"title":"抽样问题的生物启发多用途方法","authors":"Anton Tolstikhin","doi":"10.3390/computation11120254","DOIUrl":null,"url":null,"abstract":"Currently, the sampling problem has gained wide popularity in the field of autonomous mobile agent control due to the wide range of practical and fundamental problems described with its framework. This paper considers a combined decentralized control strategy that incorporates both elements of biologically inspired and gradient-based approaches. Its key feature is multitasking, consisting in the possibility of solving several tasks in parallel included in the sampling problem: localization and monitoring of several sources and restoration of the given level line boundaries.","PeriodicalId":52148,"journal":{"name":"Computation","volume":"6 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bioinspired Multipurpose Approach to the Sampling Problem\",\"authors\":\"Anton Tolstikhin\",\"doi\":\"10.3390/computation11120254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, the sampling problem has gained wide popularity in the field of autonomous mobile agent control due to the wide range of practical and fundamental problems described with its framework. This paper considers a combined decentralized control strategy that incorporates both elements of biologically inspired and gradient-based approaches. Its key feature is multitasking, consisting in the possibility of solving several tasks in parallel included in the sampling problem: localization and monitoring of several sources and restoration of the given level line boundaries.\",\"PeriodicalId\":52148,\"journal\":{\"name\":\"Computation\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/computation11120254\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/computation11120254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

目前，采样问题在自主移动代理控制领域广受欢迎，因为其框架描述了广泛的实际和基本问题。本文探讨了一种结合了生物启发和梯度方法的分散控制策略。它的主要特点是多任务处理，包括可以并行地解决采样问题中的几个任务：定位和监控几个来源以及恢复给定的水平线边界。

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

Bioinspired Multipurpose Approach to the Sampling Problem

Currently, the sampling problem has gained wide popularity in the field of autonomous mobile agent control due to the wide range of practical and fundamental problems described with its framework. This paper considers a combined decentralized control strategy that incorporates both elements of biologically inspired and gradient-based approaches. Its key feature is multitasking, consisting in the possibility of solving several tasks in parallel included in the sampling problem: localization and monitoring of several sources and restoration of the given level line boundaries.

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

Computation Mathematics-Applied Mathematics

CiteScore

3.50

自引率

4.50%

发文量

201

审稿时长

8 weeks

期刊介绍： Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.