Abstract:

The continuous variable non-stationary systems learning model is problem for dynamic Bayesian networks(DBN) with variable structure for the problem of graph model based environment perception in autonomous control. Firstly, constant DBN structure learning model in smooth random system is discussed, and the Bayesian information criterion(BIC) score to continuous hide variable DBN and structure learning frame are researched. Secondly, on the basis of constant DBN, the fuzzy self-adapt measure algorithm is presented to learn the variable DBN structure in unsmooth random  system.

Key words: autonomous , control,  , environiment , perception,  , variablestructure , DBN,  , structure , learning,  , unsmooth , random , system