HMM Training Model Using Blending Population Diversity Based Aaptive Genetic Algorithm Title

With the parameters sensibility problem of HMM model trained by GA method, this paper firstly introduces BPD-AGA algorithm based on blending population diversity. In order to make genetic iteration process enlarge the genetic searching space and improve quality of optimal solution, this algorithm adjusts the genetic parameters adaptively by individual genotype and phenotype which determine the population diversity. Secondly, HMM training model based on BPD-AGA has been proposed. This model selects the largest contribution to blending population diversity as the competition champion by BPD-AGA adaptive selection; On the other hand, HMM global optimal solution can be guaranteed by using BPD-AGA adaptive crossover and mutation, and enlarging the genetic searching space to protect the competition champion. Thirdly, this paper mixes Baum-Welch algorithm and BPD-AGA to increase the BPD-AGA training convergence speed, eventually produces a hybrid BPD-AGA/Baum-Welch model that can improve GA from external and internal. Fourthly, the process of the above model and training algorithm applied in traffic video vehicle driving status recognition has been presented. Experiment results prove the effectiveness of the proposed model and training algorithm.