This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simul...
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ISBN:
(纸本)9781467374439
This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. system conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.
Reducing noise disturbances in the frequency segment of high frequency (HF) ground wave radar and restraining the sidelobes of strong targets that interfere with the detection of weak targets are the interesting Topic...
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ISBN:
(纸本)9789955690184
Reducing noise disturbances in the frequency segment of high frequency (HF) ground wave radar and restraining the sidelobes of strong targets that interfere with the detection of weak targets are the interesting Topic. A new method based on an adaptive techniques that solves these problems is proposed. By changing the working time of the frequency spectrum monitor (FSM), we have shown not only that radar can run in the frequency segments with lower noise disturbances, but also that the noise data produced by FSM can be exploited effectively. There is no correlation between the noise and the useful echo signal, though the correlation between noises over very short time periods is strong,. Exploiting the phenomena, we can adjust system parameters in real-time by adaptive methods to solve the two problems,namely sidelobe disturbance of strong targets and noise distrubance in the frequency segment.
Petri net is an important tool to model and analyze concurrent systems, but Petri net models are frequently large and complex, and difficult to understand and modify. Slicing is a technique to remove unnecessary parts...
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Online shopping integrating third-party payment platforms (TPPs) has rapidly developed recently. The integration leads to new security problems derived from complex interactions among Application Programming Interface...
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In this paper, a multi-sensor based perception network for vehicle driving assistance is described. The network could reconstruct the 3D real world from the data obtained by the sensors, recognize dangerous occasions ...
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We attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field over how many phases during its lifetime. We ta...
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In order to strengthen the traditional information system's capacity of describing practical problems, preference relation is introduced into information system and the concepts of preference information system, p...
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Incremental feature extraction is an essential data preprocessing technique for large-scale and streaming data mining. Among various covariance matrix-free Incremental Principal Component Analysis (IPCA) methods, Cand...
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In this paper, a large-scale human action recognition system is proposed which is built upon the combination of the rising big data processing technology Spark and the powerful Graphics Processing Unit (GPU) in order ...
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This paper proposes a new method for finding principal curves from complex distribution dataset. Motivated by solving the problem, which is that existing methods did not perform well on finding principal curve in comp...
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ISBN:
(数字)9783642162480
ISBN:
(纸本)9783642162473
This paper proposes a new method for finding principal curves from complex distribution dataset. Motivated by solving the problem, which is that existing methods did not perform well on finding principal curve in complex distribution dataset with high curvature, high dispersion and self-intersecting, such as spiral-shaped curves, Firstly, rudimentary principal graph of data set is created based on the thinning algorithm, and then the contiguous vertices are merged. Finally the fitting-and-smoothing step introduced by Kegl is improved to optimize the principal graph, and Kegl's restructuring step is used to rectify imperfections of principal graph. Experimental results indicate the effectiveness of the proposed method on finding principal curves in complex distribution dataset.
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