Autoregressive State Prediction Model Based on Hidden Markov and the Application
Autoregressive State Prediction Model Based on Hidden Markov and the Application
复制标题
基于隐马尔可夫的自回归状态预测模型及应用
DOI：
10.1007/s11277-018-5259-7
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发表时间：
2018-03
影响因子：
2.2
通讯作者：
刘金国
中科院分区：
文献类型：
--
作者：
赵志国;王业琴;冯梦琦;彭光琴;刘金国
考虑到在表征前后相对相对相关的动态过程中传统隐藏的马尔可夫模型（HHM）的不准确性，一种基于隐藏的Markov的自回归状态预测模型，具有自回归MOD
Considering the inaccuracies of the traditional Hidden Markov Model (HHM) in the dynamic processes that are close relatively related before and after characterization, an autoregressive state prediction model based on Hidden Markov with Autoregressive model and the coefficient of AR is proposed, which takes the coefficient of AR as the observations of the continuous HHM. Taking the recognition and prediction of heavy vehicle driving states as the research object, a two-layer HMM model is set up to describe the state of the whole steering process of the vehicle. The AR model is for the features extracting of the observations in a short period of time, and the coefficient of AR is extracted as the observed sequence of the lower HMM model library. The upper HMM is used to identify and predict the overall state of the vehicle during steering. The proposed model makes the state sequence with the highest probability on-line predicted in the observed sequence by the Viterbi algorithm, and calculates the state transition law to predict the state of the vehicle in a certain period of time in the future using the Markov prediction algorithm. Combining the double lane change and hook steering to train the parameters of the model, the online identification and prediction of heavy vehicle rollover states can be achieved. The results show that the proposed model can accurately identify the driving state of the vehicle with good real-time performance, and the good prediction on the trend of heavy vehicle driving conditions is verified.
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DOI：
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发表时间：
2015
期刊：
--
影响因子：
--
作者：
Tian-ju Zhu
通讯作者：
Tian-ju Zhu
DOI：
10.3724/sp.j.1004.2013.02131
发表时间：
2014-03
期刊：
Acta Automatica Sinica
影响因子：
--
作者：
Xiangqun Wang;Zhihuan Cong;Lingling Fang;Junlin Qin
通讯作者：
Xiangqun Wang;Zhihuan Cong;Lingling Fang;Junlin Qin
DOI：
10.11896/j.issn.1002-137x.2016.05.055
发表时间：
2016
期刊：
计算机科学
影响因子：
--
作者：
Jing Fan;Tihong Ruan;Jiamin Wu;Tianyang Dong
通讯作者：
Jing Fan;Tihong Ruan;Jiamin Wu;Tianyang Dong
影响因子：
1.3
作者：
通讯作者：
--
影响因子：
--
作者：
Dongdong Wang
通讯作者：
Dongdong Wang