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[求助]
求帮忙查询会议文章检索号，已知被检索了
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求牛人帮忙查询一下检索号： "Multi-step prediction of frequency hopping sequences based on Bayesian inference." Information and Communications Technologies (IETICT 2013), IET International Conference on. IET, 2013. 万分感激！ |
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紫缨涵
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飞廉望舒
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诗梦用户: 金币+10, ★★★★★最佳答案, 多谢帮忙 2014-03-21 09:05:18
oven1986: 检索EPI+1, 感谢应助，鼓励一下。 2014-03-21 15:30:29
感谢参与，应助指数 +1
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oven1986: 检索EPI+1, 感谢应助，鼓励一下。 2014-03-21 15:30:29
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Accession number:20141017419148 Title:Multi-step prediction of frequency hopping sequences based on Bayesian inference Authors:Wang, Wensheng (1); Yang, Youlong (1); Li, Yanying (2) Author affiliation  1) School of Science, Xidian University, Xi'an 710071, China; (2) Department of Mathematics, Baoji University of Arts and Sciences, Baoji, China Source title:IET Conference Publications Abbreviated source title:IET Conf Publ Volume:2013 Issue:618 CP Monograph title:IET International Conference on Information and Communications Technologies, IETICT 2013 Issue date:2013 Publication year:2013 Pages:94-99 Language:English ISBN-13:9781849196536 Document type:Conference article (CA) Conference name:IET International Conference on Information and Communications Technologies, IETICT 2013 Conference date:April 27, 2013 - April 29, 2013 Conference location:Beijing, China Conference code:102858 Publisher:Institution of Engineering and Technology, Six Hills Way, Stevenage, SG1 2AY, United Kingdom Abstract:According to the chaotic characteristics of frequency hopping (FH) sequences and the short-term predictability of Chaos, this paper presents an improved Bayesian network predictive model applied to FH sequences prediction. Firstly, the model regards the entire reconstructed phase space as a prior data information; Then, according to the characteristic of FH sequences which consist of multiple frequency points, it constructs a local Bayesian network with the mutual information and an algorithm for Markov boundary; Finally, it achieves the multi-step prediction of FH by using the posterior inference algorithm. Theoretical results and large number of experiments show that the proposed Bayesian network predictive model has steady, real-time, effective and high-precision multi-step prediction ability, especially in small data set. Thus this model provides a novel method for the research and application of FH sequences prediction. Number of references:14 Main heading:Forecasting Controlled terms:Algorithms - Bayesian networks - Frequency hopping - Inference engines - Phase space methods Uncontrolled terms:Chaotic characteristics - FH sequences - Frequency hopping sequences - Inference algorithm - Multi-step prediction - Predictive modeling - Reconstructed phase space - Research and application Classification code:716.1 Information Theory and Signal Processing - 723.4.1 Expert Systems - 921 Mathematics - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory Database:Compendex Compilation and indexing terms, Copyright 2013 Elsevier Inc. 是否有用？楼主。 |
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