Affiliations: [a] School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, China | [b] School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China | [c] Jiangsu Agricultural Internet of Things Perception and System Control Engineering Laboratory, Nanjing, Jiangsu, China | [d] School of Electronics Information Engineering, Nantong Vocational University, Nantong, Jiangsu, China
Correspondence:
[*]
Corresponding author: Zhongzhi Liao, School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China. E-mail: [email protected].
Abstract: In order to improve the accuracy of the state prediction model, a dynamic Bayesian network state prediction model based on the relationship of prediction variables is designed. The prediction model of dynamic Bayesian network structure learning algorithm was improved, integrated into the Gibbs sampling algorithm model prediction, joined the predicted relationship between different factors affecting the node, is given based on the variable relationship between the dynamic Bayesian network structure design, using a moment on the different nodes and state influence factors to predict the probability distribution of the moment state nodes. The experimental results show that the model is simple in structure, more accurate than the traditional learning method of Bayesian network structure, and more practical.
Keywords: Dynamic Bayesian network, designing of networks structure, the state prediction model, the probability distribution
