|
|
【答案】应助回帖
★ ★ ★ ★ ★ ★ ★ ★ ★ ★
感谢参与，应助指数 +1
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
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。 |
|
回复此楼