Mineral Resources Evaluation Model Research Based on PSO Algorithm and BP Neural Network

2015 ◽  
Vol 12 (17) ◽  
pp. 6257-6266
Author(s):  
Xuejun Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Mineral Resources ◽  
Pso Algorithm ◽  
Model Research ◽  
Resources Evaluation

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

An Improved BP Neural Network Algorithm and its Application in Competitive Advantage Evaluation of Logistics Enterprises

2013 ◽  
Vol 850-851 ◽  
pp. 788-791
Author(s):  
Feng Lan Luo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Competitive Advantage ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Research Field ◽  
Competitive Advantages ◽  
Evaluation Indicator ◽  
Logistics Enterprises

BP neural network is a hot research field for its powerful simulation calculation ability in various disciplines in recent years, but the algorithm has some shortages such as low convergence which limit the usage of the algorithm. The paper improves BP model with genetic algorithm and applies it to evaluate competitive advantages of logistics enterprises. First the paper designs an evaluation indicator system of competitive advantage of logistics enterprises through analyzing the characteristics of the evaluation indicator; Second, genetic algorithm is used to speed up the convergence of BP algorithm and based on this the paper advances a new competitive advantage evaluation model for logistics enterprises. Finally, the improved model is realized with the data from four Chinese logistics enterprises and the realization of the experimental results show that the model can improve algorithm efficiency and evaluation accuracy and can be used for evaluating the competitive advantages of logistics enterprises practically.

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