an adaptive approach to increase accuracy of forward algorithm for solving evaluation problems on unstable statistical data set

nowadays, hidden markov models are extensively utilized for modeling stochastic processes. these models help researchers establish and implement the desired theoretical foundations using markov algorithms such as forward one. however, using stability hypothesis and the mean statistic for determining the values of markov functions on unstable statistical data set has led to a significant reduction in the accuracy of markov algorithms including forward algorithm used in solving evaluation problems. the model’s parameters such as the occurrence probability of observation symbol being produced by state, varies directly among the successive events. since the probability value of the above-mentioned parameter plays an important role in the accurate evaluation and assessment of the probability of observations’ occurrence in the evaluation problem by forward algorithm, the variations between events and observations generated by the states should be automatically extracted. in order to achieve this, the current paper proposes an adaptive parameter for event probability in order to match and adjust the variations in the parameter after each event during the lifetime of forward algorithm. the results of the experiments on a real set of data indicates the superior performance of the proposed method compared to other conventional methods regarding their accuracy.