Evaluation Algorithm of Skilled Talents’ Quality Based on Deep Belief Network Model

2022 ◽  
Author(s):  
Jiang Hua ◽  
Sun Tao
Keyword(s):  
Network Model ◽  
Quality Evaluation ◽  
Local Extremum ◽  
Deep Belief Network ◽  
Convergence Speed ◽  
Evaluation Index System ◽  
Bp Network ◽  
Belief Network ◽  
System A ◽  
Evaluation Algorithm

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.

An ontology-based deep belief network model

Computing ◽  
2021 ◽  
Author(s):  
Xiulei Liu ◽  
Ruoyu Chen ◽  
Qiang Tong ◽  
Zhihui Qin ◽  
Qinfu Shi ◽  
...  
Keyword(s):  
Network Model ◽  
Deep Belief Network ◽  
Belief Network

scholarly journals A Cycle Deep Belief Network Model for Multivariate Time Series Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shuqin Wang ◽  
Gang Hua ◽  
Guosheng Hao ◽  
Chunli Xie
Keyword(s):  
Time Series ◽  
Network Model ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Deep Belief Network ◽  
Learning Ability ◽  
Series Data ◽  
Temporal Data ◽  
Belief Network ◽  
Data Objects

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

scholarly journals Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19

2020 ◽  
Vol 39 (6) ◽  
pp. 8713-8721
Author(s):  
Luo Yuan ◽  
Zhao Xiaofei ◽  
Qiu Yiyu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Empirical Method ◽  
Bp Network ◽  
Art Teaching ◽  
The Impact

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.

Sign in / Sign up

Export Citation Format

Share Document