网络约束和通信受限下有限时间分布式优化算法及应用研究

批准号:
62003084
项目类别:
青年科学基金项目
资助金额:
24.0 万元
负责人:
时欣利
依托单位:
学科分类:
控制理论与技术
结题年份:
2023
批准年份:
2020
项目状态:
已结题
项目参与者:
时欣利
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中文摘要
随着人工智能和网络科学的蓬勃发展,数据和系统的复杂性越来越高。面对大规模复杂网络优化问题,已有的分布式计算方法往往基于理想通信下设计且收敛速度较慢。如何进一步降低通信、计算成本以及提高算法的求解效率成为目前分布式优化领域亟待解决的难题。本项目围绕网络约束和通信受限环境下快速求解网络优化问题展开,研究内容涉及网络受限环境下分布式优化问题最优化分解模型构建、网络动力学系统有限时间稳定性分析、有限时间分布式优化算法设计,以及在智能电网领域中的应用。项目研究有望突破现有计算模式,在分布式协同控制思想指导下,建立一套网络受限环境下有限时间分布式优化的理论框架。研究成果将进一步丰富现代网络系统优化理论,为解决实际工程领域中的大规模复杂优化问题提供理论方法支撑。
英文摘要
With the rapid development of artificial intelligence and network science, the complexity of data and system is getting higher and higher. In the face of large-scale complex network optimization problems, existing distributed computing methods are often designed based on ideal communication and suffer slow convergence speed. How to further reduce the communication and calculation costs and improve the efficiency of the algorithm has become an urgent problem to be tackled in the field of distributed optimization. This project focuses on fast distributed algorithms for solving network optimization problems under network and communication constraints, the research involves the construction of optimal decomposition model for distributed optimization problems in a network-constrained environment, the finite-time stability analysis of network dynamical systems, the design of finite-time distributed optimization algorithms, and applications in the field of smart grids. It is expected to break through the mode of existing computation methods, and under the guidance of the idea of distributed cooperative control, establish a theoretical framework for finite-time distributed optimization in a network-constrained environment. The research results will further enrich the modern networked system optimization theory, and provide theoretical and technical support for solving large-scale complex optimization problems in the real-world engineering field.
期刊论文列表
专著列表
科研奖励列表
会议论文列表
专利列表
DOI:10.1109/tsp.2023.3250839
发表时间:2023-02
期刊:IEEE Transactions on Signal Processing
影响因子:5.4
作者:Luyao Guo;Xinli Shi;Jinde Cao;Zihao Wang
通讯作者:Luyao Guo;Xinli Shi;Jinde Cao;Zihao Wang
DOI:10.1109/tnse.2021.3115891
发表时间:2022
期刊:IEEE Transactions on Network Science and Engineering
影响因子:6.6
作者:Wanjie Zhu;Jinde Cao;Xinli Shi;Leszek Rutkowski
通讯作者:Leszek Rutkowski
DOI:10.1109/tcsi.2023.3332109
发表时间:2024-05
期刊:IEEE Transactions on Circuits and Systems I: Regular Papers
影响因子:--
作者:Yuan Zhou;Xinli Shi;Luyao Guo;Guanghui Wen;Jinde Cao
通讯作者:Yuan Zhou;Xinli Shi;Luyao Guo;Guanghui Wen;Jinde Cao
Finite-Time and Fixed-Time Synchronization of Delayed Memristive Neural Networks via Adaptive Aperiodically Intermittent Adjustment Strategy
通过自适应非周期间歇调整策略实现延迟忆阻神经网络的有限时间和固定时间同步
DOI:10.1109/tnnls.2022.3151478
发表时间:2022-03-01
期刊:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
影响因子:10.4
作者:Cheng, Liyan;Tang, Fangcheng;Qiu, Jianlong
通讯作者:Qiu, Jianlong
DOI:10.1109/tnnls.2022.3208086
发表时间:2022-09
期刊:IEEE Transactions on Neural Networks and Learning Systems
影响因子:10.4
作者:Luyao Guo;Xinli Shi;Jinde Cao;Zihao Wang
通讯作者:Luyao Guo;Xinli Shi;Jinde Cao;Zihao Wang
国内基金
海外基金
