In this paper, a modified method for landslide prediction is presented. This method is based on the back propagation neural network(BPNN), and we use the combination of genetic algorithm and simulated annealing algori...
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This paper proposes a dependency-enhanced pre-reordering method for Chinese-English statistical machine translation(SMT).Firstly,two kinds of dependency structure-based rules are extracted based on the source-side dep...
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ISBN:
(纸本)9781509009107
This paper proposes a dependency-enhanced pre-reordering method for Chinese-English statistical machine translation(SMT).Firstly,two kinds of dependency structure-based rules are extracted based on the source-side dependency tree and corresponding word alignments between the source-side and the target-side *** a maximum entropy classifier is used to calculate the orientation probability in terms of swap or monotone between two *** a result,a reordering rule set is *** different ways are proposed to filter out the rule ***,the dependency parsing trees of the training data,development set and the test set are traversed,and if the syntactic sub-tree structure matches the rules in the rule set,the word orders will be ***,a reordered source-side sentence is generated and then fed into an SMT system for *** conducted on NIST Chinese-English MT data sets show that the proposed method significantly improves translation performance by 0.46 BLEU compared to the baseline system.
Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian...
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ISBN:
(纸本)9781467374439
Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian noise, however, is still a challenge. For a class of nonlinear systems where all of the candidate parameters are contained in a definite parameter set, an online parameters and sates estimation method is proposed based on particle filter and Bayes theorem as the following steps. Firstly, regarding all of the candidates, the states are estimated by particle filter(PF) algorithm. Secondly the posterior probabilities of all of candidates are calculated according to the Bayes theorem;then the weights of all of the candidates are obtained through normalization. Lastly, the parameters and sates are estimated ultimately according to the weighted sum of all of the candidates and states. Numerical illustrations are presented to exhibit the application of the method proposed herein, and the performance of the method is examined.
The protocol stack plays a critical role in determining the performance of networked control system (NCS), which governs the communication activities and directly affects the communication quality of service (QoS). Fu...
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With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWMVSC) have been investigated. Conventional PI controller has sh...
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To achieve better performance with various load and system parameters in controlling a current-source rectifier (CSR) with less computing cost, a neural-network-based implementation of three-logic space-vector modulat...
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Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. In this paper, a feature extraction method of electroencephalograph...
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The protocol stack plays a critical role in determining the performance of Networked control System (NCS), which governs the communication activities and directly affects the communication Quality of Service (QoS). Fu...
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In this paper, a method for a sort of nonlinear system identification with stochastic time-varying parameter is investigated. This kind of nonlinear systems is referring to the system where probability density functio...
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ISBN:
(纸本)9781509009107
In this paper, a method for a sort of nonlinear system identification with stochastic time-varying parameter is investigated. This kind of nonlinear systems is referring to the system where probability density functions(PDFs) of the parameters are known. This parameter identification and states estimation method is realized based on expectation maximization(EM) algorithm and particle filter. Firstly, parameter particles are generated randomly according to the PDF of parameter. Secondly, the particle filter is employed to estimate system states corresponding to each group of the parameters, and the weight of each group parameters is calculated according to the Bayesian theory. Then the new iteration of parameter is obtained by adopting the expectation maximization algorithm. Lastly, the real parameters are obtained along with system operation. Numerical illustrations are presented to exhibit the effectiveness of the method proposed herein, and the performance of the method is examined.
作者:
Wang, XiaolingSu, HoushengWang, XiaofanLiu, BoDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Automation
Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China College of Science
North China University of Technology Beijing100144 China
In this paper, we investigate the leader-following consensus of second-order multi-agent systems with nonlinear dynamics and time delay by employing periodically intermittent pinning control. All member agents and the...
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