belief network
Recently Published Documents





2022 ◽  
Vol 72 ◽  
pp. 103320
Jingyu Han ◽  
Guangpeng Sun ◽  
Xinhai Song ◽  
Jing Zhao ◽  
Jin Zhang ◽  

Jiang Hua ◽  
Sun Tao

In order to solve the problem that the evaluation algorithm is easy to fall into local extremum, which leads to slow convergence speed, a skilled talent quality evaluation algorithm based on a deep belief network model was designed. Establish an evaluation set with 4 first level indicators and 14 second level indicators, and calculate the corresponding weights to complete the construction of the evaluation index system. A DBN structure composed of several RBMs and a BP network is constructed. Based on the DBN, a quality evaluation algorithm is designed. The algorithm training is used to evaluate the test data and output the evaluation level. The experimental results show that the convergence speed of DBN based evaluation algorithm is significantly better than that of BP neural network and SVM based evaluation algorithm under the same number of iterations, which is suitable for the accurate evaluation of talent quality.

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Xiao Tian ◽  
Niankun Zhu

To truly reflect the durability characteristics of concrete subjected to multiple factors under complex environmental conditions, it is necessary to discuss the prediction of its durability. In response to the problem of durability prediction of traditional concrete structures, there is a low prediction accuracy, and the predicted time is long, and a concrete structural durability prediction method based on the deep belief network is proposed. The influencing factors of the concrete structural durability parameters are analyzed by two major categories of concrete material and external environmental conditions, and the transmission of chloride ions in the concrete structure is described. According to the disconnection of the steel bars, the durability of the concrete structure is started, and the determination is determined. The concrete structural antiflexural strength, using a deep belief network training concrete structural antiflexural strength judgment data, constructs a concrete structural durability predictive model and completes the durability prediction of the concrete structure based on the deep belief network. The proposed prediction method based on the deep belief network has a high prediction accuracy of 98% for the durability of concrete column structures. The simulation results show that the concrete structural durability’s prediction accuracy is high and the prediction time is short. The prediction of concrete durability discussed here has important guiding significance for the improvement of concrete durability test methods and the improvement of concrete durability evaluation standards in China.

Today’s global and complex world increased the vulnerability to risks exponentially and organizations are compelled to develop effective risk management strategies for its mitigation. The prime focus of research is to design a supply risk model using Bayesian Belief Network bear in mind the tie-in of risk factors (i.e. objective and subjective) those are critical to a supply chain network. The proposed model can be re-engineered as per new information available at disclosure, so risk analysis will be current and relevant along the timeline as so situation is strained. The top three factors which influenced profitability were transportation risk and price risks. Netica is the platform used for designing and running simultaneous simulations on the Bayesian Network. The proposed methodology is demonstrated through a case study conducted in an Indian manufacturing supply chain taking inputs from supply chain/risk management experts. .

Sign in / Sign up

Export Citation Format

Share Document