基于加权HMM的车辆电源系统状态预测

针对车辆电源系统状态趋势问题，提出了一种加权隐马尔可夫模型的状态预测方法。通过建立电源系统的隐马尔可夫模型，利用加权预测思想对隐马尔可夫模型中隐状态序列进行预测，将最大概率隐状态利用观测概率密度计算出状态观测值。通过对电压调节脉宽信号的导通率进行预测，并与BP神经网络和自回归（AR）模型对相同序列的预测结果进行对比，结果表明该方法对系统的状态变化具有较好的预测能力。

Vehicle power system condition prediction based on weighted hidden Markov model

A new condition prediction approach based on weighed Hidden Markov Model (HMM) was presented in order to solve the problem of trend prediction for vehicle power system. Through the establishment of the power system HMM, the hidden state of HMM was predicted by weighted prediction method, and the observed state of the model was calculated by the observation probability density of the maximum probability hidden state. The approach was applied to the state prediction of rate turn of the system voltage adjusting pulse width signals, and was compared to that of using BP (Back Propagation) neural network and Auto-Regression (AR) prediction model with the same sequence. The results show that the method has better prediction on the system state change.