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arXiv 2025年

作者： Tian, Wan Qin, Zhongfeng School of Economics and Management Beihang University Beijing100191 China Key Laboratory of Complex System Analysis Management and Decision Beihang University Ministry of Education Beijing100191 China

Modeling and forecasting interval-valued time series (ITS) have attracted considerable attention due to their growing presence in various contexts. To the best of our knowledge, there have been no efforts to model large-scale ITS. In this paper, we propose a feature extraction procedure for large-scale ITS, which involves key steps such as auto-segmentation and clustering, and feature transfer learning. This procedure can be seamlessly integrated with any suitable prediction models for forecasting purposes. Specifically, we transform the automatic segmentation and clustering of ITS into the estimation of Toeplitz sparse precision matrices and assignment set. The majorization-minimization algorithm is employed to convert this highly non-convex optimization problem into two subproblems. We derive efficient dynamic programming and alternating direction method to solve these two subproblems alternately and establish their convergence properties. By employing the Joint Recurrence Plot (JRP) to image subsequence and assigning a class label to each cluster, an image dataset is constructed. Then, an appropriate neural network is chosen to train on this image dataset and used to extract features for the next step of forecasting. Real data applications demonstrate that the proposed method can effectively obtain invariant representations of the raw data and enhance forecasting performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Research on Synthetic Data Generation and Multi-modal Target Detection

Research on Synthetic Data Generation and Multi-modal Target...

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第34届中国控制与决策会议

作者： Jiexi Liu Chenyu Wang Lei Xu Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision

Target Detection is one of the most important tasks in Computer Vision,which has broad application prospects in many *** the past few years,great progress has been made in this field with the rapid development of deep learning ***,it's still a big challenge at complex scenarios such as dim environment for traditional single-modal visible *** address this problem,researchers introduce thermal images as additional modal in consideration of that thermal cameras are less susceptible to interference and explore how to fuse the two modalities information ***,the lack of large labeled and high-quality visible-thermal datasets hampers the usage of convolutional neural networks for ***,we propose to use image-to-image translation model combined with a differentiable data augmentation method to generate fake thermal images from labeled visible images and use multi-modal target detection model to prove the validity of the *** experiment results show that our method can provide us a large labeled dataset of synthetic visible-thermal image pairs with better generalization and the introduction of thermal modality can obtain a better performance than single modality.

关键词： Image-to-image translation Multi-modal target detection Deep learning

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Global attracting set and asymptotic behavior for a class of impulsive functional Hopfield neural networks by a novel vector inequality

Global attracting set and asymptotic behavior for a class of...

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Chinese Control and decision Conference, CCDC

作者： Weisong Zhou Guihua Li Wei Zhu Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing P. R. China

This article is concerned with a class of nonlinear and nonautonomous functional Hopfield neural networks with impulsive effects. The existence of attracting set and invariant set of the desired impulsive differential equations is established. Firstly, we construct a novel vector inequality, which considers the features of M-matrix. Based on the more general vector inequality, we ensure the positive invariant set and global attracting set for a class of the impulsive functional differential equations. By means of numerical simulation, the effectiveness of our theoretical results is demonstrated.

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Exponential p-stability of stochastic impulsive fuzzy reaction-diffusion Cohen-Grossberg neural networks with mixed delays

Exponential p-stability of stochastic impulsive fuzzy reacti...

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第34届中国控制与决策会议

作者： Weisong Zhou Sa Xiao Wei Zhu Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications

In this paper,the pth moment exponential stability of a class of stochastic impulsive fuzzy reaction-diffusion Cohen-Grossberg neural networks(CGNNs) with mixed delays is *** means of the property of "M-cone",Lyapunov functional,Ito formula and Hanalay inequality techniques,sufficient conditions for exponential p-stability of our model are ***,a simulation example is given to verify the validity of our result.

关键词： Exponential stability Cohen-Grossberg Mixed delays Impulsive Fuzzy

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Finite-time tracking control of heterogeneous multi-AUV systems with partial measurements and intermittent communication

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Science China(Information Sciences) 2024年第5期67卷 165-182页

作者： Bo CHEN Jiangping HU Bijoy Kumar GHOSH School of Automation Engineering University of Electronic Science and Technology of China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Department of Mathematics and Statistics Texas Tech University

The development of a distributed trajectory-tracking control strategy that is independent of velocity measurements is critical to achieving finite-time tracking control of autonomous underwater vehicle(AUV) systems. In this study, a group of heterogeneous AUV systems with intermittent communication links is considered and a finite-time trajectory-tracking control strategy is developed. The strategy includes two observers and one controller proposed for each follower-AUV. The first observer, a hybrid finite-time observer,estimates the state of the leader, whereas the second observer, which relies only on the position measurement,is proposed to estimate the states of the follower-AUV itself. In addition, a distributed trajectory-tracking controller is designed using the states estimated by the intermittent communication network even without velocity measurements. A homogeneous technique is utilized to prove that all followers can track the leader in a finite time. Finally, the effectiveness of the developed finite-time tracking control strategy is illustrated by numerical simulations.

关键词： distributed observer finite-time tracking homogeneous technique intermittent communication multiple heterogeneous AUVs

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Predicting graphical models based on functional data

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International Journal of Approximate Reasoning 2025年 186卷

作者： Wu, Qiying Wang, Huiwen School of Economics and Management Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations Beijing100191 China Key Laboratory of Complex System Analysis Management and Decision Beihang University Ministry of Education Beijing100191 China

Graphical models are widely used to model complex relationships between variables in various fields. However, existing analysis methods focus primarily on scalar data and give little attention to addressing the challenges posed by nonscalar data, e.g., functional data, which are prevalent in many real-world applications. Additionally, most methods assume a static graphical model within the observed period, neglecting the dynamic changes that may occur over time. In this paper, we propose a novel method for predicting graphical models based on functional data. Our approach transforms functional data into finite-dimensional vectors via basis function expansion and cross-validation. We then establish a sequential prediction model for determining the correlation coefficient matrix of the decomposed data, thus accounting for the constraints imposed on the matrix via a transformation technique. Finally, we employ conditional independence tests to identify the edges of the predicted graphical model. We demonstrate the effectiveness of our method through extensive simulations and real data analyses. The results show that our method performs better than the competing methods in terms of prediction accuracy and provides valuable insights into the dynamic changes exhibited by a network. This work opens new possibilities for conducting graphical model analyses in various domains, particularly in terms of handling functional data and predicting dynamic relationships. © 2025 Elsevier Inc.

关键词： Time series

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Parameter Inversion of High-Dimensional Chaotic systems Using Neural Ordinary Differential Equations

Parameter Inversion of High-Dimensional Chaotic Systems Usin...

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Data Driven Control and Learning systems (DDCLS)

作者： Hanlin Bian Wei Zhu Zhang Chen L. Jingsui Chao Pei Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

High-dimensional chaotic systems possess complex structural characteristics, which have potential research value in fields like encrypted communication and chaos control. The inverse problem of parameters in high-dimensional chaotic systems is of significant importance for a profound understanding of the dynamic properties of such systems. This article takes a departure from neural ordinary differential equations and embeds the structural dynamics of the dynamical systems to the parameter inversion of high-dimensional chaotic systems. Numerical experiments on different high-dimensional chaotic systems indicate that neural ordinary differential equations perform exceptionally well in solving these inversion problems, with low errors and strong robustness.

关键词： Resistance Learning systems Chaotic communication Inverse problems Noise Ordinary differential equations Robustness

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Effective distributed algorithm for solving linear matrix equations

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Science China(Information Sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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complex network analysis of global stock market co-movement during the COVID-19 pandemic based on intraday open-high-low-close data

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Financial Innovation 2024年第1期10卷 4031-4080页

作者： Wenyang Huang Huiwen Wang Yigang Wei Julien Chevallier College of Economics and Management China Agricultural UniversityBeijingChina School of Economics and Management Beihang UniversityBeijingChina Key Laboratory of Complex System Analysis Management and Decision(Beihang University)Ministry of EducationBeijingChina Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations BeijingChina IPAG Business School(IPAG Lab) 184 bd Saint-GermainParisFrance University of Paris 8(LED) 2 rue de la LibertéSaint-DenisFrance

This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)*** propose a novel method for calculating stock price index correlations based on open-high-low-close(OHLC)*** intraday information can be utilized compared with the widely used return-based *** testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial *** topologies of the global stock market complex network constructed using 70 important global stock price indices before(2017-2019)and after(2020-2022)the COVID-19 outbreak were *** evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based *** global stock market co-movement characteristics are revealed,and the financial centers of the developed,emerging,and frontier markets are *** centrality indicators,we also illustrate changes in the importance of individual stock price indices during the COVID-19 *** on these findings,we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.

关键词： complex network Stock market co-movement OHLC data Degree centrality analysis

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PI Tuning and Optimization for Speed Control of PMSM Based on Multivariable Sliding-Mode Extremum Seeking

PI Tuning and Optimization for Speed Control of PMSM Based o...

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第34届中国控制与决策会议

作者： Fan Li Wei Shen Chuanyu Ge Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology

ISBN: (纸本)9781665478977

The PI controller is the most basic and widely used for the permanent magnet synchronous motor speed *** cumbersome parameter adjustment process makes it difficult for the PI controller to balance convenience and *** this end,this paper presents a new PI tuning and optimization scheme based on multivariable sliding-mode extremum ***,by analyzing the frequency domain model of permanent magnet synchronous motor,the analytical calculation formulas of controller parameters and motor parameters are derived as the initial value of PI parameter ***,a multivariable sliding-mode extremum seeking optimizer is designed to optimize the control parameters by minimizing the cost function based on the feedback error *** feasibility of the above scheme is proved by theoretical derivation,and the simulation and experimental results also verify the feasibility and effectiveness of the proposed method.

关键词： permanent-magnet synchronous motor(PMSM) multivariable sliding mode extremum seeking(MSME) self-tuning parameter optimization

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