CAJ | 학술논문

传统的向前-向后算法或Baum-Welch算法训练HMM的转移概率aij和发射概率ai(Ot),使观察序列的O概率恰好达到最大值往往很难,虽然在理论上训练HMM的这两个网络结构是可能的,但仅能保证局部的最大值,而基于全局搜索的基因表达式编程(GEP)的一个主要的特点就是可以高效快速的发现全局最优解.把GEP引入到HMM的训练中去,提出一种改进的训练方法GBHA.实验结果表明,该算法比传统算法的系统效率更高、更稳定.
전통적향전-향후산법혹Baum-Welch산법훈련HMM적전이개솔aij화발사개솔ai(Ot),사관찰서렬적O개솔흡호체도최대치왕왕흔난,수연재이론상훈련HMM적저량개망락결구시가능적,단부능보증국부적최대치,이기우전국수색적기인표체식편정(GEP)적일개주요적특점취시가이고효쾌속적발현전국최우해.파GEP인입도HMM적훈련중거,제출일충개진적훈련방법GBHA.실험결과표명,해산법비전통산법적계통효솔경고、경은정.
According to the transfer probability aij and transmit probability ai(Ot) of HMM by traditional forward-backwards algorithm or Baum-Welch algorithms,which is possible to traning this two network structure,but is extremely difficult to estimate parameter a and b by which make sure the observational sequence O is the max.Because this method can only lead to local optimization.Since gene expression programming (GEP) based on globel search is introduced for converging to global optimization,it is applied to HMM training,a modified training method GBHA is proposed.Experimental results show that the training method proposed is more effective than tranditioal methods,the experiments also show that GBHA is not sensitive to initial value of the model,thereby the stability of the algorithm is enhanced.