混合训练的DHMM及其在发射机状态检测中的应用

隐马尔可夫模型(HMM)是一种双随机过程,其训练方法B-W算法是一种基于爬山算法,容易陷入局部最优且对初始参数值依赖性大.为了提高模型的有效性,该文提出了将改进的模拟退火(SA)算法和B-W算法相结合的混合训练方法,解决了受模型参数初值影响的问题并能实现全局搜索.将其应用于发射机功率状态检测中,实验结果证明该方法准确性高,收敛速度快和稳定性好,是一种很有实用价值的新方法.

Hybrid Training DHMM and Its Application to Check Transmitter Power

HMM model is a double random processing which is trained with B-W algorithm, this algorithm based on hill-climbing is easy to lead to locally optimal solutions and its validity is greatly depend on model initial parameters. In order to improve the validity of model, this paper proposes a hybrid training method which combine the B-W algorithm with improved SA algorithm. With this hybrid method the validity of model is not influenced by the model initial parameters and the global optimal solution can be easily gained. Applying this hybrid method to check transmitter power, the experimental results show that proposed method is practical method and own qualities such as high veracity, rapid converged and good stability.