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Stability analysis of delayed neural networks via novel delay-dependent LKF and integral inequality

ISA Transactions 2025年

作者： Fei Long Chuan-Ke Zhang Yanjun Shen Qicheng Mei Qing Chen College of Electrical Engineering and New Energy China Three Gorges University Yichang 443002 China Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station Yichang 443002 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Automation China University of Geosciences Wuhan 430074 China

The current paper is concerned with the stability analysis of delayed neural networks. In the case that the delay derivative is restricted with an upper bound only, the augmented LKFs often contain high-degree terms of the time-varying delay, resulting in the non-convex derivatives of LKFs, which can be solved by introducing extra delay-multiplied state variables to transform the non-convex delay-dependent terms into convex ones. To make fuller use of the delay-multiplied state variables and the delay-derivative-dependent information, these delay-multiplied state variables are introduced into an LKF and the integral inequality through the proper augmentation in this paper. Meanwhile, some free-matrix-based zero equations are introduced into this delay-dependent inequality to provide more freedom. By applying the augmented LKF and the novel integral inequality, a delay-dependent stability criterion of delayed neural networks with less conservatism is established, whose advantages are verified by three examples.

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Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

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complex System Modeling and Simulation 2021年第4期1卷 291-307页

作者： Zhihua Cui Lihong Zhao Youqian Zeng Yeqing Ren Wensheng Zhang Xiao-Zhi Gao School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Computing University of Eastern FinlandKuopioFI-70211Finland

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial ***,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization *** selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto ***,the accuracy,convergence,and diversity of solutions are *** is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization *** results indicate the superiority of ImMAPIO on these test functions.

关键词： pigeon-inspired optimization algorithm many-objective optimization problem multiple selection strategy elite individual retention

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A Memristor Based Chaotic Oscillator for Weak Signal Detection and Its Circuitry Realization

Research Square

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Research Square 2021年

作者： Dong, Kaifeng Xu, Kun Zhou, Youyou Zuo, Chao Wang, Leimin Zhang, Chuanke Jin, Fang Song, Junlei Mo, Wenqin Hui, Yajuan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

A new type of weak signal detection system that combines the memristor and Van der pol-Duffing chaotic system has been proposed in this paper, and the dynamic characteristics of the system are studied. It is observed that the system can change from a chaotic state to a periodic state under different driving force amplitudes. Moreover, as compared with several classical chaotic oscillators, the numerical simulation results show that the system has stronger anti-noise performance with the detectable signal-to-noise ratio reaches -163dB, and has a wider detection range. Its detection accuracy is up to 1 × 10−9. More importantly, this paper provides the circuit implementation scheme of the system, and the weak signal can be detected with our designed circuit. This may offer a possible way for weak signal detection system with good performance in anti-noise performance, detection range and accuracy. © 2021, CC BY.

关键词： Memristors

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A Reinforcement-Learning-Based Evolutionary Algorithm Using Solution Space Clustering For Multimodal Optimization Problems

A Reinforcement-Learning-Based Evolutionary Algorithm Using ...

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Congress on Evolutionary Computation

作者： Hai Xia Changhe Li Sanyou Zeng Qingshan Tan Junchen Wang Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms.

关键词： Uncertainty Clustering algorithms Evolutionary computation Reinforcement learning Benchmark testing Search problems Feature extraction

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A Novel Scalable Framework For Constructing Dynamic Multi-objective Optimization Problems

A Novel Scalable Framework For Constructing Dynamic Multi-ob...

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Congress on Evolutionary Computation

作者： Qingshan Tan Changhe Li Hai Xia Sanyou Zeng Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized; and the number of conflicting variables should be independent from the number of objectives; the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.

关键词： Schedules Analytical models Heuristic algorithms Object oriented modeling Evolutionary computation Benchmark testing Dynamic scheduling

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Adaptive control for asynchronously switched fractional-order multi-agent systems

Adaptive control for asynchronously switched fractional-orde...

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第三十九届中国控制会议

作者： Xiongliang Zhang Shiqi Zheng Feng Liu Mingyuan Zhao Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, we discuss the adaptive controller design for a class of fractional-order（FO） multi-agent systems（MAS）.All the agents are described by FO pure-feedback nonlinear systems. This is more general than the existing works, which only considered strict-feedback or integer-order nonlinear systems. Meanwhile, asynchronous switching are considered in FO MAS,meaning the agents have heterogeneous switching dynamics. A new neural network based adaptive controller is proposed for the considered FO MAS, which can make the consensus tracking error converge to a small neighborhood of origin. Simulation verifies the results obtained.

关键词： Consensus tracking fractional-order multi-agent systems asynchronously switched systems

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H∞ Consensus control for Discrete-Time Stochastic Multi-agent systems with Infinite Markov Jumps

H∞ Consensus Control for Discrete-Time Stochastic Multi-age...

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Annual Conference of Industrial Electronics Society

作者： Jie Wang Xin Chen School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

This paper focuses on multi-agent systems under the disturbances of parameter uncertainties, noises and external disturbances simultaneously, which are more suitable for the changing environments than most ideal models. For this case, the robust H∞ consensus control problem is studied to attenuate the influence of disturbances below a given level. We aim to design a consensus controller such that the closed-loop muti-agent systems reach the desired H ∞ performance. By tools of Kronecker product, graph theory, and infinite horizon bounded real lemma, sufficient conditions for existence of H ∞ consensus protocol design are obtained in terms of linear matrix inequalities (LMIs). Finally, a numerical example is given to show the validity of the method.

关键词： Multi-agent systems Symmetric matrices Markov processes Stochastic systems Robot sensing systems Eigenvalues and eigenfunctions Topology

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Mechanical Design Paradigm based on ACP Method in Parallel Manufacturing

Mechanical Design Paradigm based on ACP Method in Parallel M...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Shimeng Li Yutong Wang Xiao Wang Feiyue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

Parallel Manufacturing is a new manufacturing paradigm in industry, deeply integrating informalization, automation, and artificial intelligence. In this paper we propose a new mechanical design paradigm in Parallel Manufacturing based on ACP method. The key is to regard the design procedure based on artificial design and emulation method as two independent procedures, which can be modeled as a parallel system. The design procedure based on ACP method does not include a real system, which is an inventive extension of the traditional parallel system. This method can be implemented with social information by introducing the definition of SDV, SDM, and intelligent Design Manager, making it highly adaptive for social manufacturing and Parallel Manufacturing.

关键词： Industries Adaptation models automation Digital twin Design methodology Conferences Emulation

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An adaptive federated control strategy for participant selection in multi-client collaboration 1

An adaptive federated control strategy for participant selec...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Chen, Yizhu Du, Xiaoming Wang, Xiao Zhang, Jun Jason Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9781665433372

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %, 9 respectively. © 2021 IEEE.

关键词： Stream flow

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Chinese Calligraphy Character Generating via CGAN with a Multi-subnet Parallel and Cascade Generator

Chinese Calligraphy Character Generating via CGAN with a Mul...

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第三十九届中国控制会议

作者： Xiaoxue Zhou Ziying Zhang Xin Chen Mengxi Qin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Arts and Communication China University of Geosciences

Chinese character generation is one of the key technologies in Chinese character intelligent design. Most generation methods cannot solve the situation where the generated Chinese character strokes have artifact or even miss. The main reason is that the generator is not strong enough. Therefore, in this paper, we propose a multi-subnet parallel and cascade generator, which combines the conditional generation adversarial network（CGAN） to realize the generation of calligraphy Chinese *** method divides a Chinese character generation task into several parts undertaken by each sub-generator, and the output of each sub-generator will contain certain image information, which will increase with the rise of sub-generator hierarchy. It allows each sub-generator to focus on its own task, generating more realistic characters eventually. Finally, the test set performs better than the CGAN with a single generator.

关键词： CGAN Chinese calligraphy character generation Multi-subnet parallel and cascade generator

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