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.