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 e...
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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.
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...
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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 equation and this avoids the difficulty in solving of Hamilton-Jacobi-Isaac equations.
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 ad...
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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.< >
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...
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ISBN:
(纸本)0780319788
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 of this control is much better than that of PID control and it has good robustness.< >
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 ...
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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.
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...
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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.
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