Abstract:

To deal with ownership degree of samples in Markov chain effectively facing with a skip of the predicted probability, a cloud transforming method of Markov chain state prediction is proposed. Samples’ uncertainty is described and processed by using the cloud model. Regarded as a kind of concept, the partitioned state intervals are expressed based on the cloud model, and further the certainty of each objective to all concepts is computed. Then to realize stochastic state prediction, the concept transfer matrix is calculated. The kernel density estimation of concept transfer probability is obtained considering its significance. Finally simulation results are given in form of probability of repeated tests and extracted representative transfer probability, and it shows that the uncertain method can both avoid a skip of the Markov chain predicted probability and measure ownership degree of samples effectively, and is more practical as well.