Research on Fuzzy Buried Markov Model
Yongzhao Zhan · 2008
To avoid the disadvantage of hidden Markov model which doesn't consider the contextual relevant relationship of states and observations and the changeability of transfer probability, an improved model called fuzzy buried Markov model is put forward in this paper. Adding relationship among the different observations, resolving the problem of transfer probability uncertainty and ameliorating the parameter optimization arithmetic make the novel model be suitable to apply in pattern identification with much noise and losing of some training data. Compared with other model of the same complexity, fuzzy buried Markov model shows more good character such as optimum performance, better division degree, lower error rate and lustihood, which can be proved by graphical theory.