%0 Journal Article
%T Discrete channel modelling based on genetic algorithm and simulated annealing for training hidden Markov model
%A Zhao Zhi-Jin
%A Zheng Shi-Lian
%A Xu Chun-Yun
%A Kong Xian-Zheng
%A <br>赵知劲
%A 郑仕链
%A 徐春云
%A 孔宪正
%J 中国物理 B
%D 2007
%I 
%X Hidden Markov models (HMMs) have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm (GA) and simulated annealing (SA) to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA's ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.
%K hidden Markov model
%K discrete channel model
%K genetic algorithm
%K simulated annealing<br>隐马尔可夫模型
%K 离散信道模型
%K 遗传算法
%K 模拟退火
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=CD8D6A6897B9334F09D8D1648C376FB4&aid=51EC29F0F1FE4796E918B009336019DF&yid=A732AF04DDA03BB3&vid=7801E6FC5AE9020C&iid=B31275AF3241DB2D&sid=266729317CF80522&eid=393FA6E3D9B6498B&journal_id=1009-1963&journal_name=中国物理&referenced_num=0&reference_num=17