检索结果-内蒙古大学图书馆

Event-triggered integral sliding mode control of two-time-scale systems

NONLINEAR DYNAMICS 2025年第2期113卷 1263-1277页

作者： Yu, Tian-Tian Wang, Yan-Wu Yang, Wu Lei, Yan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China GuangXi Univ Sch Elect Engn Nanning 530004 Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Gener Technol & Syst Serv Robot Chongqing 400715 Peoples R China

This paper investigates the H-infinity performance of two-time-scale systems with nonlinear uncertainty and external disturbance via the event-triggered integral sliding mode control (ETISMC) strategy. To facilitate the analysis of the sliding mode surface's reachability and the design of the event-triggered (ET) integral sliding mode controller, the epsilon-dependent integral sliding function with the event-triggered mechanism is constructed. Based on H-infinity control theory, the integral sliding mode controller gain and the ET parameter matrix are co-designed through the solution of a set of linear matrix inequalities. The proposed event-triggered mechanism can avoid Zeno behavior. Furthermore, an adaptive ETISMC strategy is proposed to address the unknown upper bounds of uncertainty and external disturbance. Finally, the effectiveness, applicability, and advantages of the control strategies are verified by simulations and comparative analysis.

关键词： Two-time-scale system H-infinity performance Event-triggered mechanism Integral sliding mode control Adaptive control

来源：评论

学校读者我要写书评

暂无评论

A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 638-652页

作者： Lu, Renzhi Bai, Ruichang Li, Ruidong Zhu, Lijun Sun, Mingyang Xiao, Feng Wang, Dong Wu, Huaming Ding, Yuemin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Shanghai Elect Grp Co Ltd Cent Acad Shanghai 200070 Peoples R China Kanazawa Univ Inst Sci & Engn Kakuma Kanazawa Ishikawa 9201192 Japan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Univ Navarra Tecnun Sch Engn San Sebastian 20018 Spain

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders;and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

关键词： Load modeling Predictive models Load forecasting Forecasting Autoregressive processes Data models Time series analysis Decomposition strategy long short-term memory (LSTM) neural network multistep load forecasting sequence-to-sequence (Seq2Seq) model temporal convolution network (TCN)

来源：评论

学校读者我要写书评

暂无评论

Semi-Global and Global Fixed-Time Stability for Nonlinear Impulsive Systems

引用

IEEE TRANSACTIONS ON CYBERNETICS 2025年第4期55卷 1814-1824页

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Huang, Tingwen Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Texas A&M Univ Qatar Sci Program Doha Qatar

This study investigates the semi-global fixed-time stability (SGFTS) and global fixed-time stability (GFTS) of nonlinear impulsive systems (NISs). A key challenge in analyzing the SGFTS of such systems lies in the evolving integration methods caused by the impulses. To address this, we dynamically partition the semi-global attraction set (SGAS) and solve the corresponding differential equations within each subset. Additionally, by constructing the transition dynamics of impulse points and iteratively computing these points, we establish the conditions for SGFTS under both stabilizing and destabilizing impulses. For GFTS, the primary difficulty arises from the distinct trajectories and dynamics of points located inside and outside the SGAS. To overcome this, we introduce the concept of the maximum-minimum impulse interval and derive a sufficient condition that ensures the system can enter the SGAS from a distance under a finite number of impulses. Furthermore, we develop a criterion for GFTS under varying impulse degrees and provide convergence time estimation based on the research on SGFTS of NIS. Finally, numerical examples are presented to validate the theoretical results. Notably, in Example 3, a fixed-time impulse controller is designed based on the proposed theoretical framework to achieve global stabilization of complex systems. This example highlights the potential applications of this work in the field of control.

关键词： Fixed-time stability global stability nonlinear impulsive systems (NISs) nonlinear impulsive systems (NISs) semi-global stability semi-global stability semi-global stability

来源：评论

学校读者我要写书评

暂无评论

A Novel Dynamic Peer-to-Peer Carbon Right Sharing Scheme for Microgrid Clusters

引用

IEEE Transactions on Smart Grid 2025年

作者： Zhou, Wei Lai, Jingang Li, Xi Zeng, Zhigang School of Artificial Intelligence and Automation China Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

In this paper, a novel four-level distributed game decision-making framework of dynamic peer-to-peer (P2P) carbon emission right (CER) sharing is proposed for microgrid clusters (MGCs), which consists of four phases: exploration, selection, equilibrium and correction. Firstly, in the exploration stage, the tradable information is published by microgrids (MGs) and an initial set of tradable options is constructed. Secondly, in the selection stage, bilateral transaction coefficients are calculated according to the deficit and surplus of CERs in the P2P carbon market, and then an optimal sharing matching scheme of CERs is designed to reduce invalid transactions. Thirdly, in the equilibrium stage, a P2P carbon right sharing strategy based on the Stackelberg game is proposed to realize the dynamic change in carbon prices and the flexible conversion of master-slave roles in P2P carbon trading, which can promote the popularity of carbon market. Fourthly, in the correction stage, a carbon emission right balancer (CERB) is developed to avoid the imbalance of CERs, and then a billing scheme including incentives and penalties is established to achieve the benefit redistribution after P2P carbon trading. In the end, numerical results illustrate the effectiveness of the proposed multi-layer game decision-making framework for P2P carbon right sharing among MGs. © 2010-2012 IEEE.

关键词： carbon emission right balancer Microgrid cluster peer-to-peer carbon trading Stackelberg game

来源：评论

学校读者我要写书评

暂无评论

Correlation-aware constrained many-objective service composition in crowdsourcing design

引用

ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Li, Gui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

With the development of social productivity, crowdsourcing design, a new service-oriented innovative design model emerged. The optimal selection of service resources in crowdsourcing design has a great impact on the quality of task implementation and consumer satisfaction. Unlike the previous research, this paper models and solves the problem from the perspective of constrained many-objective optimization. Firstly, a constrained 6objective combination optimization model is constructed according to the characteristics of service resources in crowdsourcing design. Secondly, a variety of correlation categories and correlation forms of services are considered. It is not limited to the correlation between two services but rather correlations between any number of services. In order to improve search efficiency, a multi-objective service pruning (MOSP) method based on efficient non-dominated sorting (ENS) is used to reduce the decision space. The new constraint strategy handles complex constraints, and a correlation-aware local search strategy is proposed to deal with correlations between services. The results of comparison experiments prove the superiority of the proposed method in solving the constrained correlation-aware many-objective service composition problem.

关键词： Many-objective optimization Dynamic learning evolutionary algorithm Crowdsourcing design Service composition Constraint optimization

来源：评论

学校读者我要写书评

暂无评论

A transactive energy cooperation scheduling for hydrogen-based community microgrid with refueling preferences of hydrogen vehicles

引用

APPLIED ENERGY 2025年 377卷

作者： Zhang, Xiao-Yan Wang, Cenfeng Xiao, Jiang-Wen Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China State Grid Zhejiang Elect Power Co Ltd Econ & Technol Res Inst Hangzhou 310008 Peoples R China

Hydrogen, as a high specific energy green carrier, is promising for power generation and transportation. This paper proposes a transactive energy cooperation framework for an end-user-oriented hydrogen-based community microgrid considering refueling preferences of hydrogen vehicles (HVs) and industrial hydrogen demand to minimize social cost. In this framework, an aggregator obtains stacking profits in both the electricity and hydrogen markets through flexible interconversion, storage, and interaction of electricity and hydrogen. For resident users, the refueling preferences of HVs are analyzed to reduce cost through demand response (DR). A novel energy density-weighted asymmetric Nash bargaining (DANB) method is designed to fairly assess the combined contribution, considering both electricity and hydrogen bargaining metrics for end- users. An alternating optimization procedure with the adaptive alternating direction method of multipliers (ADMM) algorithm is designed to solve the mixed-integer linear programming (MILP) problem in the social operation cost minimization process. Comparison results show the algorithm convergence advantage, the cost-effectiveness and scalability of the cooperation framework, as well as the rationality of the DANB and the flexibility of HVs in DR. Besides, the optimal configuration of the battery, the hydrogen tank, and the electrolyzer in the framework is explored, alongside the economic feasibility of electricity-hydrogen-electricity technology.

关键词： Hydrogen-based microgrid Hydrogen demand response Combined contribution assessment Alternating optimization procedure with alternating direction method of multipliers

来源：评论

学校读者我要写书评

暂无评论

Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design

引用

IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 719-733页

作者： Lin, Jianqing He, Cheng Tian, Ye Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

关键词： Optimization Computational modeling Iron Convergence Linear programming Predictive models Particle swarm optimization Kernel Costs Clustering algorithms Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction

来源：评论

学校读者我要写书评

暂无评论

A radial visualization method based on knee point information for many-objective optimization

引用

APPLIED SOFT COMPUTING 2025年 175卷

作者： Li, Gui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Numerous high-dimensional solutions for many-objective optimization problems (MaOPs) usually impose a high cognitive burden on decision makers (DMs). Pareto front (PF) of MaOPs can express the problem characteristics, and then provide prior knowledge for solving the MaOPs. However, the existing high-dimensional visualization methods usually do not establish the relationship between PF information and decision making. Therefore, a novel radial visualization (RadViz) method called KRadViz that incorporates knee point information is proposed to visualize the information of PF shape and aid decision making. The relationship between the optimized performance information and PF shape is established, and the PF shape identification method is constructed. KRadViz is constructed by combining the optimization performance and PF shape. Three preferred solution selection methods are proposed to quickly screen out a few preferred solutions in different scenarios. The proposed KRadViz is compared with three high-dimensional visualization methods. The experimental results show that KRadViz can effectively display the high-dimensional PF shape, and give the optimization performance information of different solutions. The selection preferences of the three methods are also analyzed, and the effectiveness of the assisted decision process is verified. For the DTLZ2 and real-world MaOPs, the individual hypervolume (HV) contribution of preferred solutions increased by 9.98 % and 10.95 %, respectively.

关键词： Many-objective optimization High-dimensional visualization Knee points Preferred information RadViz

来源：评论

学校读者我要写书评

暂无评论

Dynamic Bilinear Fusion Network for Synthetic Aperture Radar image Change Detection

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Dong, Huihui Du, Xinyu Li, Zhijie Li, Xiaohuan Ma, Zongfang Gao, Feng Jiao, Licheng Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

Change detection from synthetic aperture radar (SAR) imagery is critical in remote sensing research. Existing methods have made significant progress in the application of convolutional neural networks (CNNs) and attention mechanisms. However, traditional CNNs suffer the limitations in feature representation due to their depth and width constraints, and struggle to effectively capture complex interactions between image features. To address these issues, we propose a novel dynamic bilinear fusion network (DBFNet) for change detection in SAR imagery. First, to compensate for the lack of traditional convolutional representation capability, we design a dynamic shift convolution module that adaptively aggregates multiple convolution kernels and shifts pixels, enabling richer and more detailed features to be extracted. Second, a bilinear fusion module (BFM) is designed to generate the bilinear joint representation between parallel features by computing a matrix outer product of feature maps. The parallel features include both intraimage and interimage features, thereby effectively modeling the complex interactions and capturing the dependence relationship between spatiotemporal features. The experimental results on three real SAR datasets demonstrate the superior performance of DBFNet compared to existing state-of-the-art methods.

关键词： Convolution Feature extraction Radar polarimetry Logic gates Kernel Synthetic aperture radar Convolutional neural networks Adaptation models Transformers Rivers Bilinear pooling change detection dynamic convolution synthetic aperture radar (SAR) image

来源：评论

学校读者我要写书评

暂无评论

Complementary Learning Subnetworks Towards Parameter-Efficient Class-Incremental Learning

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第6期37卷 3240-3252页

作者： Li, Depeng Zeng, Zhigang Dai, Wei Suganthan, Ponnuthurai Nagaratnam Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha 2713 Qatar

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. To mitigate the catastrophic forgetting phenomenon, typical CIL methods either cumulatively store exemplars of old classes for retraining model parameters from scratch or progressively expand model size as new classes arrive, which, however, compromises their practical value due to little attention paid to parameter efficiency. In this paper, we contribute a novel solution, effective control of the parameters of a well-trained model, by the synergy between two complementary learning subnetworks. Specifically, we integrate one plastic feature extractor and one analytical feed-forward classifier into a unified framework amenable to streaming data. In each CIL session, it achieves non-overwritten parameter updates in a cost-effective manner, neither revisiting old task data nor extending previously learned networks;Instead, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Experimental results on a variety of task sequences demonstrate that our method achieves competitive results against state-of-the-art CIL approaches, especially in accuracy gain, knowledge transfer, training efficiency, and task-order robustness. Furthermore, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated to make its non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks.

关键词： Adaptation models Feature extraction Training Data mining Knowledge engineering Data models Accuracy Vectors Transformers Robustness Non-stationary data streaming data modeling class-incremental learning complementary learning system

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：