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RISS 인기검색어
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- 저자 : Runan Guo (검색결과 2 건)
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Runan Guo,Ziye Zhang,Chong Lin,Yuming Chu,Yongmin Li 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.3
This paper considers the finite time state estimation problem of complex-valued bidirectional associativememory (BAM) neutral-type neural networks with time-varying delays. By resorting to the Lyapunov functionapproach, the Wirtinger inequality and the reciprocally convex approach, a delay-dependent criterion in terms ofLMIs is established to guarantee the finite-time boundedness of the error-state system for the addressed system. Meanwhile, an effective state estimator is designed to estimate the network states through the available outputmeasurements. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed results.
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An efficient reliability analysis strategy for low failure probability problems
Runan Cao,Zhili Sun,Jian Wang,Fanyi Guo 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.78 No.2
For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.
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