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Adaptive control of a Chaotic System with Delay

Chinese Physics Letters 1998年第7期15卷 477-479页

作者： TIAN Yu-chu National Key Laboratory of Industrial Control Technology Institute of Industrial Process ControlZhejiang UniversityHangzhou 310027

A model-reference adaptive control strategy is proposed for a delay chaotic system with known or unknown *** analysis and numerical simulations show that the controlled system state can track an arbitrarily given refe... 详细信息

关键词： parameters chaotic system

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A new necessary and sufficient condition for static output feedback stabilizability and comments on "stabilization via static output feedback"

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IEEE Transactions on Automatic control 1998年第8期43卷 1110-1111页

作者： Cao, Yong-Yan Sun, You-Xian Mao, Wei-Jie Institute of Industrial Process Control National Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

In this paper, a counterexample of the above-mentioned paper⊥ is reported. It is pointed out that one of the conditions for a linear system to be stabilizable via static output feedback is not correct. A modified nec... 详细信息

In this paper, a counterexample of the above-mentioned paper^⊥ is reported. It is pointed out that one of the conditions for a linear system to be stabilizable via static output feedback is not correct. A modified necessary and sufficient condition for this problem is also presented.

关键词： Stabilization Static output feedback

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Robust H∞ control for linear time-delay systems subject to norm-bounded nonlinear uncertainty

Robust H∞ control for linear time-delay systems subject to ...

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1998 American control Conference, ACC 1998

作者： Gu, Yongru Geng, Chenge Qian, Jixin Wang, Leyu National Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 China Dept. of Optical and Scientific Instrument Zhejiang University China

ISBN: (纸本)0780345304

This paper focuses on the problem of robust H∞ control for linear uncertain time-delay systems with norm-bounded nonlinear uncertainty in both the state and control. Also, the time delay is time-varying and can exist both in the state and control. Using the fact that the type of nonlinear uncertainty set considered here has an equivalent representation by linear uncertainty set, we propose an approach for designing a memoryless state feedback control law which will stabilize the time-delay systems and, simultaneously, guarantee a prespecified H∞ disturbance attenuation constraint for all admissible uncertainty. © 1998 AACC.

关键词： Time delay

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Self-learning fuzzy PID controller based on neural networks

Self-learning fuzzy PID controller based on neural networks

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American control Conference (ACC)

作者： Qiqiang Li Zhengqun Cheng Jixin Qian National Laboratory of Industrial Control Technology Institute of Industrial Process Control University of Zhejiang Hangzhou China

For conventional PID tuner and fuzzy inference systems based on expertise problem will arise when expertise of the process is not enough. Artificial neural networks have self-learning capability, however, the change of their weights can not be understood, This paper describes the structures of self-learning neuro-fuzzy networks and shrinking-span membership functions, and presents a neuro-fuzzy PID (NFPID) controller. The NFPID controller has the capability of self-extracting inference rules, and its parameters have explicitly physical definitions. By using the RBF neural network inverse model, a hybrid learning procedure was put forward. Various simulation results demonstrated that the NFPID controller described has very good performances.

关键词： Fuzzy control Fuzzy neural networks Three-term control Neural networks Fuzzy systems Input variables industrial control Laboratories Electrical equipment industry process control

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Robust H/sub /spl infin// control for linear time-delay systems subject to norm-bounded nonlinear uncertainty

Robust H/sub /spl infin// control for linear time-delay syst...

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American control Conference (ACC)

作者： Yongru Gu Chenge Geng Jixin Qian Leyu Wang National Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou China Department of Optical and Scientific Instrument University of Zhejiang China

Focuses on the problem of robust H/sub /spl infin// control for linear uncertain time-delay systems with norm-bounded nonlinear uncertainty in both the state and control. Also, the time delay is time-varying and can exist both in the state and control. Using the fact that the type of nonlinear uncertainty set considered here has an equivalent representation by linear uncertainty set, we propose an approach for designing a memoryless state feedback control law which will stabilize the time-delay systems and, simultaneously, guarantee a prespecified H/sub /spl infin// disturbance attenuation constraint for all admissible uncertainty.

关键词： Robust control Nonlinear control systems control systems Uncertainty Delay systems Attenuation Riccati equations Linear systems Symmetric matrices industrial control

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Robust H/sub /spl infin// control for systems with sector nonlinear uncertainty in actuators

Robust H/sub /spl infin// control for systems with sector no...

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American control Conference (ACC)

作者： Yongru Gu Chenge Geng Jixin Qian Leyu Wang National Laboratory of Industrial Control Technology Institute of Industrial Process Control University of Zhejiang Hangzhou China Department of Optical and Scientific Instrument University of Zhejiang China

Focuses on the problem of robust H/sub /spl infin// feedback control law design for a class of systems with sector nonlinear uncertainty in actuators. The control law can be obtained by solving an algebraic Riccati eq... 详细信息

关键词： Robust control Nonlinear control systems control systems Uncertainty Actuators Uncertain systems Loss measurement State feedback Linear matrix inequalities Symmetric matrices

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Recurrent neural networks for recursive identification of nonlinear dynamic process

Recurrent neural networks for recursive identification of no...

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IEEE International Conference on industrial technology (ICIT)

作者： Jia-Wen Dong Ji-Xin Qian You-Xian Sun Institute of Industrial Process Control National Industrial Process Control Key Laboratory University of Zhejiang Hangzhou China Industrial Process Control National Industrial Process Control Key Laboratory Zhejiang University Hangzhou China

ISBN: (纸本)0780319788

In this paper, modified Elman-type recurrent neural networks (1990) were developed to identify the dynamic nonlinear systems with generalised backpropagation recursive algorithm. Analysis shows that introduction of adjustable self-connections of context units provides network ability to model high order input-output mapping, unbiased estimates can be achieved without the need to fit additive noise model. An industrial application example shows its efficiency.< >

关键词： Recurrent neural networks Multi-layer neural network Neural networks Electrical equipment industry industrial control process control Context modeling System identification Parameter estimation Neurofeedback

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The design and application of the generalized predictive control in the strong exothermic reactor

The design and application of the generalized predictive con...

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IEEE International Conference on industrial technology (ICIT)

作者： Chen Dan Qian Jixin Sun Youxian Dong Jiawen Institute of Industrial Process Control National Industrial Process Control Key Laboratory University of Zhejiang Hangzhou China

This paper presents the design and application of the generalized predictive control (GPC) in a strong exothermic reactor. Through simulation the effect of GPC is tested. The results of tests show that the performance... 详细信息

ISBN: (纸本)0780319788

关键词： Predictive control Inductors Coolants Heat transfer Testing industrial control process control Robustness Open loop systems Three-term control

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TFGIN: Tight-Fitting Graph Inference Network for Table-based Fact Verification

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ACM Transactions on Information Systems 1000年

作者： Lianwei Wu Kang Wang Kunlin Nie Sensen Guo Chao Gao Zhen Wang Shudong Li School of Computer Science Northwestern Polytechinical University China The 15th Research Institute of China Electronics Technology Group Corporation China Xidian University China Northwestern Polytechinical University China the Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security and Huangpu Research School Guangzhou University China

Fact verification task has emerged as an essential research topic recently due to abundant fake news spreading on the Internet. The task based on unstructured data (i.e., news) has achieved great development, but the task based on structured data (i.e., table) is still in the primary development period. The existing methods usually construct complete heterogeneous graph networks around statement, table, and program subgraphs, and then infer to learn similar semantics on them for fact verification. However, they generally connect the nodes with the same content between subgraphs directly to frame a larger graph network, which has serious sparsity in connections, especially when subgraphs possess limited semantics. To this end, we propose tight-fitting graph inference network (TFGIN), which innovatively builds tight-fitting graphs (TF-graph) to strengthen the connections of subgraphs, and designs inference modeling layer (IML) to learn coherence evidence for fact verification. Specifically, different from traditional connection ways, the constructed TF-graph enhances inter-graph and intra-graph connections of subgraphs through subgraph segmentation and interaction guidance mechanisms. Inference modeling layer could reason the semantics with strong correlation and high consistency as explainable evidence. Experiments on three competitive datasets confirm the superiority and scalability of our TFGIN.

关键词： Fact Verification Table-based Verification Natural Language processing

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AEKG4APT: An AI-Enhanced Knowledge Graph for advanced Persistent Threats with Large Language Model Analysis

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ACM Transactions on Intelligent Systems and technology 1000年

作者： Yinghai Zhou Ziyu Wang Yunxin Jiang Bingqi Ma Rui Wang Yuan Liu Yue Zhao Zhihong Tian Cyberspace Institute of Advanced Technology Guangzhou University China National Key Laboratory of Security Communication China Cyberspace Institute of Advanced Technology Guangzhou University with the Guangdong Key Laboratory of Industrial Control System Security and also with Huangpu Research School of Guangzhou University China

This paper introduces AEKG4APT, an APT Knowledge Graph (KG) enhanced by Large Language Models (LLMs), as a way to deal with the cybersecurity problems caused by advanced Persistent Threats (APTs). The core of AEKG4APT lies in the combined application of LLMs, Cyber Threat Intelligence (CTI), and KG. The first part of the paper goes into great detail about how the AEKG4APT was constructed, including its ontology schema, data sources, and dataset features. There are also statistics on the AEKG4APT’s nodes, relationships, and key attributes. Secondly, it was shown how to utilize LLMs and public sandboxes for the collection and analysis of CTI Additionally, tests that compare traditional deep learning models to LLM methods show that LLM is both more efficient and more accurate at extracting information. Subsequently, the Decision Making Trial and Evaluation laboratory - Interpretive Structural Modeling (DEMATEL-ISM) analytical method was introduced to identify and analyse the factors and their interrelationships within the AEKG4APT data, thereby revealing the key dependencies and influence paths within the data structure. Experiments were designed to demonstrate its applications in modeling, computing, and obtaining interpretable computational results on AEKG4APT. In addition, this paper also explores the dynamic expansion capabilities of AEKG4APT, including data expansion, schema expansion, and permanent maintenance strategies, to address the evolving APT threats. Finally, this paper summarizes the competitiveness and application value of AEKG4APT by comparing it with other CTI KGs and platforms in academia and industry, demonstrating its extensive application potential in the field of cybersecurity.

关键词： advanced Persistent Threat Large Language Models Knowledge Graph Cyber Threat Intelligence Sandboxes DEMATEL-ISM

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