检索结果-内蒙古大学图书馆

Dynamic-Based Privacy Preservation for Distributed Economic Dispatch of Microgrids

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 1029-1039页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Lu, Qingguo Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

With an escalating emphasis on distributed economic dispatch within microgrid systems due to its inherent adaptability, scalability, and sustainability, an extensive focus on the confidentiality of this field is pronouncedly emerging. The primary emphasis of this study is the safeguarding of power-sensitive information in the distributed economic dispatch issue prevalent in microgrids. This pursuit leads us to the development of a distributed optimization algorithm that preserves privacy, tailored for directed networks. The algorithm strives to secure a balance between supply and demand at the lowest economic cost, all while adhering to real-world constraints and maintaining the confidentiality of power-sensitive information. To fulfill this objective, we propose a novel privacy-preserving distributed algorithm that capitalizes on the inherent resilience exhibited by system dynamics toward uncertainty. Specifically, to ensure privacy preservation, we strategically incorporate randomness into the mixing weights, thereby generating a degree of uncertainty in communication messages during the initial iteration. Rigorous analysis is built to delineate that our method can achieve exact convergence and ensure the confidentiality of power-sensitive information. Further, additional numerical trials conducted on an IEEE 14-bus system substantiate the algorithm's practical efficiency.

关键词： Privacy Microgrids Costs Convergence Uncertainty Cost function Supply and demand Consensus optimization economic dispatch (ED) microgrids privacy preservation

来源：评论

学校读者我要写书评

暂无评论

Observer-Based Event-Triggered Adaptive Control for nonlinear Multiagent Systems With Unknown States and Disturbances

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 6663-6669页

作者： Pang, Ning Wang, Xin Wang, Ziming Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Guangdong Prov Key Lab Intelligent Decis & Coopera Guangzhou 510006 Peoples R China

Based on radial basis function neural networks (RBF NNs) and backstepping techniques, this brief considers the consensus tracking problem for nonlinear semi-strict-feedback multiagent systems with unknown states and disturbances. The adaptive event-triggered control scheme is introduced to decrease the update times of the controller so as to save the limited communication resources. To detect the unknown state, external disturbance, and reduce calculation workload, the state observer and disturbance observer as well as the first-order filter are first jointly constructed. It is shown that all the output signals of followers can uniformly track the reference signal of the leader and all the error signals are uniformly bounded. A simulation example is carried out to further prove the effectiveness of the proposed control scheme.

关键词： Event-triggered control multiagent system observer tracking control

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems

引用

IEEE TRANSACTIONS ON SIGNAL AND informatION proCESSING OVER NETWORKS 2024年 10卷 820-832页

作者： Tang, Daocheng Pang, Ning Wang, Xin Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuitsand Intelligen Chongqing 400715 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.

关键词： Constrained optimized backstepping (OB) containment control denial-of-service (DoS) attacks. dynamic event-triggered mechanism (DETM) reinforcement learning (RL)

来源：评论

学校读者我要写书评

暂无评论

Robust exponential stabilization of stochastic coupled T-S fuzzy complex networks subject to state-dependent impulsive control

引用

INTERNATIONAL JOURNAL OF ROBUST AND nonlinear CONTROL 2023年第5期33卷 3334-3357页

作者： Cao, Zhengran Li, Chuandong Zhang, Xiaoyu Yang, Xujun Southwest Univ Intelligent Informat Proc Sch Elect & Informat Chongqing Key Lab Nonlinear Circuits Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Dept Math Chongqing Peoples R China

The problem of robust exponential stabilization for T-S fuzzy stochastic dynamical networks (T-S FSDNs) based on the state-dependent impulsive controller is investigated in the paper. Using B-equivalence approach, some sufficient conditions are provided such that every solution of considered systems intersects each impulsive surface exactly once. Then, combining with the inequality technique and comparison principle, some meaningful sufficient criteria are obtained to ensure the mean square stability of T-S fuzzy stochastic dynamical networks. Compared to related results, the results obtained in the paper are low conservatism and easy to verify. Finally, two numerical simulations based on Ro$$ \ddot{o} $$ssler's system and Lorenz system show the effectiveness of the obtained theoretical results.

关键词： B-equivalent approach complex networks robust stabilization T-S fuzzy model state-dependent impulsive control

来源：评论

学校读者我要写书评

暂无评论

Tensor-based unsupervised feature selection for error-robust handling of unbalanced incomplete multi-view data

引用

informatION FUSION 2025年 114卷

作者： Yang, Xuanhao Che, Hangjun Leung, Man-Fai Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge England

Recent advancements in multi-view unsupervised feature selection (MUFS) have been notable, yet two primary challenges persist. First, real-world datasets frequently consist of unbalanced incomplete multi-view data, a scenario not adequately addressed by current MUFS methodologies. Second, the inherent complexity and heterogeneity of multi-view data often introduce significant noise, an aspect largely neglected by existing approaches, compromising their noise robustness. To tackle these issues, this paper introduces a Tensor-Based Error Robust Unbalanced Incomplete Multi-view Unsupervised Feature Selection (TERUIMUFS) strategy. The proposed MUFS framework specifically caters to unbalanced incomplete multi-view data, incorporating self- representation learning with a tensor low-rank constraint and sample diversity learning. This approach not only mitigates errors in the self-representation process but also corrects errors in the self-representation tensor, significantly enhancing the model's resilience to noise. Furthermore, graph learning serves as a pivotal link between MUFS and self-representation learning. An innovative iterative optimization algorithm is developed for TERUIMUFS, complete with a thorough analysis of its convergence and computational complexity. Experimental results demonstrate TERUIMUFS's effectiveness and competitiveness in addressing unbalanced incomplete multi-view unsupervised feature selection (UIMUFS), marking a significant advancement in the field.

关键词： Multi-view unsupervised feature selection Unbalanced incomplete multi-view data Self-representation learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Control for Uncertain nonlinear Systems With Dynamic Full State Constraints: The SMDO Approach

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第3期53卷 1710-1722页

作者： Pang, Ning Wang, Xin Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Intelligent Decis & Cooper Guangzhou 510006 Peoples R China

This article focuses on the adaptive control issue for uncertain nonlinear systems with time-varying full-state constraints. First, a novel integral barrier Lyapunov functions (IBLFs)-based neural backstepping control approach is designed, which circumvents the trouble of conversion in the traditional used BLFs. And then, the sliding-mode disturbance observers (SMDOs) are established to deal with the immeasurable disturbances in each order of the state-constrained uncertain nonlinear systems. Besides, the dynamic threshold-based event-sampling mechanism is constructed to deal with the sparsity of resources and system controlling burden. Finally, according to the given design approach, an event-triggered adaptive controller is developed and ensures disturbance observation errors uniformly converge to the origin in finite time, and all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. A developed numerical simulation case verifies the validity of the proposed approach.

关键词： Adaptive control Adaptive systems Control systems nonlinear dynamical systems Asymptotic stability Time-varying systems Stability analysis Adaptive control full state constraints sliding-mode observer uncertain nonlinear system

来源：评论

学校读者我要写书评

暂无评论

Distributed Neurodynamic Models for Solving a Class of System of nonlinear Equations

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 486-497页

作者： Han, Xin He, Xing Ju, Xingxing Che, Hangjun Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Sichuan Univ Arts & Sci Coll Math Dazhou 635000 Sichuan Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China Texas A&M Univ Qatar Dept Math Doha Qatar

This article investigates a class of systems of nonlinear equations (SNEs). Three distributed neurodynamic models (DNMs), namely a two-layer model (DNM-I) and two single-layer models (DNM-II and DNM-III), are proposed to search for such a system's exact solution or a solution in the sense of leastsquares. Combining a dynamic positive definite matrix with the primal-dual method, DNM-I is designed and it is proved to be globally convergent. To obtain a concise model, based on the dynamic positive definite matrix, time-varying gain, and activation function, DNM-II is developed and it enjoys global convergence. To inherit DNM-II's concise structure and improved convergence, DNM-III is proposed with the aid of time-varying gain and activation function, and this model possesses global fixed-time consensus and convergence. For the smooth case, DNM-III's globally exponential convergence is demonstrated under the Polyak-Lojasiewicz (PL) condition. Moreover, for the nonsmooth case, DNM-III's globally finite-time convergence is proved under the Kurdyka-Lojasiewicz (KL) condition. Finally, the proposed DNMs are applied to tackle quadratic programming (QP), and some numerical examples are provided to illustrate the effectiveness and advantages of the proposed models.

关键词： Mathematical models Convergence Neurodynamics Minimization Numerical models Linear programming nonlinear equations Distributed neurodynamic models (DNMs) fixed-time consensus and finite-time convergence Kurdyka-ojasiewicz (KL) and Polyak-ojasiewicz (PL) conditions nonlinear equations

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition

引用

IEEE TRANSACTIONS ON SIGNAL AND informatION proCESSING OVER NETWORKS 2024年 10卷 513-526页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Lu, Qingguo Zhao, You Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, resource allocation, etc. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this may either sacrifice optimization accuracy or incur a heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, named PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. When the objective function is smooth and satisfies the Polyak-& Lstrok;ojasiewicz (PL) condition, PPSD attains an R-linear convergence rate. Moreover, the algorithm can preserve the normal agents' private information from being leaked to honest-but-curious attackers. Simulations further confirm the results.

关键词： Optimization Privacy Heuristic algorithms Linear programming Convergence Machine learning algorithms information processing Privacy protection decentralized optimization push-pull state decomposition

来源：评论

学校读者我要写书评

暂无评论

Distributed Online Private Learning of Convex Nondecomposable Objectives

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1716-1728页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

In the machine learning domain, datasets share several new features, including distributed storage, high velocity, and privacy concerns, which naturally requires the development of distributed privacy-preserving algorithms. Moreover, nodes (e.g., learners, sensors, GPUs, mobiles, etc.) in the real networks usually process tasks in real time, which inevitably requires nodes to have online learning capabilities. Therefore, we deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and the goal of all nodes is to collaboratively minimize the global cost over a time horizon $T$ while guarantees the security of the transmitted information. For such problems, we first design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method. Note that in the dual updates, all nodes of DPSDA employ the noise-corrupted gradients for more generality. Then, we propose two algorithms, named as DPSDA-C and DPSDA-PS, under this framework. In DPSDA-C, the nodes implement a circulation-based communication in the primal updates so as to alleviate the disagreements over time-varying undirected networks. In addition, for the extension to time-varying directed ones, the nodes implement the broadcast-based push-sum dynamics in DPSDA-PS, which can achieve average consensus over arbitrary directed networks. Theoretical results show that both algorithms attain an expected regret upper bound in O(root T) when the objective function is convex, which matches the best utility achievable by cutting-edge algorithms. Finally, numerous numerical experiments on both synthetic and real-world datasets verify the effectiveness of our algorithms.

关键词： Differential privacy nondecomposable problem distributed online learning time-varying networks

来源：评论

学校读者我要写书评

暂无评论

projection neural networks with finite-time and fixed-time convergence for sparse signal reconstruction

引用

NEURAL COMPUTING & APPLICATIONS 2024年第1期36卷 425-443页

作者： Xu, Jing Li, Chuandong He, Xing Zhang, Xiaoyu Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Beijing Foreign Studies Univ Sch Informat Sci & Technol Beijing 100080 Peoples R China

This paper considers the L-1-minimization problem for sparse signal and image reconstruction by using projection neural networks (PNNs). Firstly, a new finite-time converging projection neural network (FtPNN) is presented. Building upon FtPNN, a new fixed-time converging PNN (FxtPNN) is designed. Under the condition that the projection matrix satisfies the Restricted Isometry property (RIP), the stability in the sense of Lyapunov and the finite-time convergence property of the proposed FtPNN are proved;then, it is proven that the proposed FxtPNN is stable and converges to the optimum solution regardless of the initial values in fixed time. Finally, simulation examples with signal and image reconstruction are carried out to show the effectiveness of our proposed two neural networks, namely FtPNN and FxtPNN.

关键词： Sparse reconstruction Finite-time convergence Fixed-time convergence projection neural networks L-1-minimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：