Logistics service quality evaluation model of B2C mode network shopping based on the AHP-BP neural network

2016 ◽  
pp. 833-837
Author(s):  
C Deng ◽  
P Sun ◽  
R Pan
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Logistics Service ◽  
Service Quality Evaluation ◽  
Logistics Service Quality ◽  
Quality Evaluation Model

Agricultural Information Service Quality Evaluation Algorithm Based on Genetic Algorithm, BP Neural Network and Multiple Regressions

2013 ◽  
Vol 433-435 ◽  
pp. 713-719
Author(s):  
Cheng Chen ◽  
Hua Rui Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Service Quality ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Information Service ◽  
Bp Network ◽  
Service Quality Evaluation ◽  
Quality Evaluation Model

Information service objects in agriculture relatively have a complex demand due to agricultural regional and seasonal. The construction of information service quality evaluation model contributes to analyze the influencing factors that influence the quality of information service, proving guidance for agricultural information service. Combined with genetic Algorithm, BP neural network and multiple regression, a hybrid BP network based on the integration of BP Network and multiple regression models is proposed, and the initial weights of hybrid BP network is optimized by hybrid genetic algorithm, effectively avoid the flaws when these methods used separately. Proved by the experiment, information service quality evaluation model constructed by a hybrid BP network based on the optimization of genetic Algorithm has a good accuracy and generalization ability, the mean error within 5%.

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.

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