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Distributed Alternating Direction Method of Multipliers for Multi-Objective Optimization

Advances in Pure Mathematics 2022年第4期12卷 249-259页

作者： Hui Deng Yangdong Xu Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China

In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.

关键词： Alternating Direction Method of Multipliers Distributed Algorithm Multi-Objective Optimization Multi-Agent System

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A Resilient Data-Driven Controller Against DoS Attacks 41

A Resilient Data-Driven Controller Against DoS Attacks

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第41届中国控制会议

作者： Wenjie Liu Jian Sun Gang Wang Jie Chen Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Department of Control Science and Engineering Tongji University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper is concerned with the stabilization problem of linear time-invariant systems under Denial-of-Service(DoS)***,system matrices are unknown,and only some input-output trajectories collected from off-line experiments are ***,in the model-based case,it has been proven that systems equipped with a predictor-based state feedback controller achieve resilience against DoS ***,this is difficult or even impossible to be implemented in the absence of a system model,thus leading to resilient control challenging in the data-based *** maintain resilience against DoS attacks in the data-based case,a data-driven model predictive control(MPC) scheme is proposed such that future system input-output trajectories can be obtained by solving a data-dependent optimal *** this scheme,a data-driven resilient control strategy is developed such that the system achieves the same level of resilience of the DoS attacks as the model-based ***,a numerical example is given to validate the effectiveness of the proposed method.

关键词： Denial-of-service attack data-driven control model predictive control input-to-state stability

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Trapezoidal pythagorean fuzzy linguistic bonferroni mean operator based on the preference relation: A case study for targeted poverty alleviation 14

Trapezoidal pythagorean fuzzy linguistic bonferroni mean ope...

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14th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Xiao, Yue Xian, Sidong School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781538680971

The 19th session of national congress of the communist party of China reaffirmed its determination to win the fight against poverty. Therefore, it is of great importance to determine how to help people in different poverty-stricken areas. Bonferroni mean (BM), which can capture interrelationship among the individual arguments. And the intuitionistic preference relation is a powerful mean to express decision makers' information of intuitionistic preference over criteria in the process of decision making. Hence, in this paper, we present weighted trapezoidal Pythagorean fuzzy linguistic preference relation (TrPFLPR), afterwards, we propose the BM into trapezoidal Pythagorean fuzzy linguistic preference environment, present the trapezoidal Pythagorean fuzzy linguistic Bonferroni mean (TrPFLBM) operator and the weight TrPFLBM operator. In conclusion, a numerical instance about targeted poverty alleviation selection is given to demonstrate the method's effectiveness. © 2018 IEEE.

关键词： decision making

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A Ranking Model for Intuitionistic Fuzzy Preference Relation Under Uncertainty for Targeted Poverty 15th

A Ranking Model for Intuitionistic Fuzzy Preference Relation...

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15th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Yu, Dongxu Xian, Sidong School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783030324551

Intuitionistic fuzzy preference relation (IFPR) is a suitable tool to present preference information and hesitation for decision maker among poverty alleviation candidates. This paper aims to provide an effective decision method for helping decision maker accurately to obtain priority weights by using IFPRs to represent the proportion of importance from candidates and rank them. Firstly, a fractional interactive minimizing deviation programming model based on multiplicative consistency-based method to derive weight vectors of alternatives from IFPRs based on multiplicative consistency is presented. Specifically, for any IFPR, by minimizing its absolute deviation from the corresponding consistent IFPR, the weight vectors are generated. Then, a new score function based on Euclidean distance is proposed. This defuzzification value is computed by constructing the idea of closest to ideal solution based on distance measure and ranks alternatives. Subsequently, a numerical example about targeted poverty alleviation selection is given to demonstrate the methods effectiveness. © Springer Nature Switzerland AG, 2020.

关键词： decision making

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Photoelectric intelligent Detection Platform Based on Multifunctional Edge Computing Equipment 43

Photoelectric Intelligent Detection Platform Based on Multif...

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43rd Chinese Control Conference, CCC 2024

作者： Xu, Qianjing Chen, Wenjie Xue, Zhijun State Key Lab of Intelligent Control and Decision of Complex Systems Beijing10081 China Beijing Institute of Technology School of Automation Beijing100081 China

ISBN: (纸本)9789887581581

In order to study to design a photoelectric intelligent platform based on multifunctional edge computing equipment by using photoelectric detection equipment TC505C, RK-Series Development Kits computing platform and Nvidia Jetson Series. The photoelectric intelligent platform can be used to run multiple target detection algorithms after design and implementation. Completed as follows. First of all, after the first stage of the photoelectric intelligent platform is completed, the host computer can establish communication with the photoelectric detection equipment, carry out automatic or manual servo control on the photoelectric detection equipment, switch its visible and infrared field of view and view it in real time through the monitor, and realize simple tracking function through the image processing box. Secondly, after the second stage of the photoelectric intelligent platform is built, under the premise of ensuring the normal availability of servo function, a variety of target detection algorithms can be debugged on multifunctional edge computing equipment. Overall, this paper uses the modular design concept to design, debug and implement the system in stages. Pay attention to the synchronous debugging of hardware and software functions of each module to ensure the stability of each module. Finally completed the design goals of each stage. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Program debugging

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Time-Varying Formation Control for Multiagent systems with Different Time Delays 9

Time-Varying Formation Control for Multiagent Systems with D...

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9th International Conference on Fuzzy systems and Data Mining, FSDM 2023

作者： Sun, Fenglan Liang, Qinbo Zhu, Wei Li, Qiuying Key Lab of Intelligent Analysis and Decision on Complex Systems School of Science Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781643684703

This article studies the time-varying formation (TVF) problem of multiagent systems (MASs) with different time delays. By designing the control protocol, the followers could achieve the desired TVF. Considering different communication delays, based on graph theory and stability theory, sufficient conditions for the system to realize TVF are obtained. Finally, the numerical example is employed to validate the effectiveness of theoretical results. © 2023 The authors and IOS Press.

关键词： Multi agent systems

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Reinforcement Learning Applied to Agent Path Planning 41

Reinforcement Learning Applied to Agent Path Planning

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第41届中国控制会议

作者： Fenglan Sun Xiaoshuai Wu Guoxiong Long Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Department of Complexity Science Potsdam Institute for Climate Impact Research

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper provides an enhanced Q-learning algorithm for agent path planning in traditional grid maps,which solves the problem of agent path planning in grid *** an unknown environment,the classic Q-learning algorithm addresses the agent's path planning ***,there are several limits to this strategy in terms of path planning:the agent could only move nearby grids,and the step size is only one *** Q-learning algorithm,including changes the agent's direction and step *** action direction of agents is increased from four to *** movement step of agents is raised from one to three *** new method makes the convergence faster and proxy path ***,a set of simulation tests are presented to validate the modified Q-learning algorithm.

关键词： Agent Path planning Q-learning Grid method

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Detection of Stealthy False Data Injection Attacks Against Cyber-Physical systems:A Stochastic Coding Scheme

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Journal of systems Science & complexity 2022年第5期35卷 1668-1684页

作者： GUO Haibin PANG Zhonghua SUN Jian LI Jun State Key Lab of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongging Innovation Center Chongqing 401120China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China

This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector without other defensive *** detect such a class of FDI attacks,a stochastic coding scheme,which codes the sensor measurement with a Gaussian stochastic signal at the sensor side,is proposed to assist an anomaly detector to expose the FDI *** order to ensure the system performance in the normal operational context,a decoder is adopted to decode the coded sensor measurement when received at the controller *** this detection scheme,the residual under the attack can be significantly different from that in the normal situation,and thus trigger an *** design condition of the coding signal covariance is derived to meet the constraints of false alarm rate and attack detection *** minimize the trace of the coding signal covariance,the design problem of the coding signal is converted into a constraint non-convex optimization problem,and an estimation-optimization iteration algorithm is presented to obtain a numerical solution of the coding signal covariance.A numerical example is given to verify the effectiveness of the proposed scheme.

关键词： Attack detection cyber-physical systems(CPSs) stealthy FDI attacks stochastic coding

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Model Reconstruction Based on Physical-Informed Polynomial Neural ODEs 13

Model Reconstruction Based on Physical-Informed Polynomial N...

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13th IEEE Data Driven Control and Learning systems Conference, DDCLS 2024

作者： Chen, Zhang Zhu, Wei Li, Jingsui Bian, Hanlin Pei, Chao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing400065 China

ISBN: (纸本)9798350361674

Neural networks are capable function approximators for data, but their lack of interpretability poses challenges. Physical-informed Polynomial Neural Ordinary Differential Equations (PIPNODEs) are proposed. They combine the constraints of prior knowledge of the data with interpretable Polynomial Neural Ordinary Differential Equations (PolyNODEs) to discover or approximate the governing equations. In this work, Neural Ordinary Differential Equations (NODEs) are categorized into two layers: a physical-informed term, which represents the priori knowledge of the data, and a discrepancy term, which is derived from the approximation of PolyNODEs. Pruning strategies are also used to remove redundant terms with coefficients lower than a certain threshold. We demonstrate the effectiveness and flexibility of this framework in a series of numerical algorithms with varying levels of prior knowledge. We found that having correct but incomplete physical information accelerates training and approaching thresholds compared to having incorrect physical information. © 2024 IEEE.

关键词： data modeling discprepancy modeling interpretability Neural Ordinary Differential Equations physics-informed machine learning polynomial neural network

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Parameter Inversion of High-Dimensional Chaotic systems Using Neural Ordinary Differential Equations 13

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

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13th IEEE Data Driven Control and Learning systems Conference, DDCLS 2024

作者： Bian, Hanlin Zhu, Wei Chen, Zhang Jingsui, L. Pei, Chao Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350361674

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. © 2024 IEEE.

关键词： Inverse problems

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