scholarly journals Comprehensive Evaluation Model of Infectious Disease Epidemic Degree Based on PCA and BP Neural Network

2020 ◽  
Vol 07 (05) ◽  
pp. 56-61
Author(s):  
Wang Zhili ◽  
Ding Xuanyi
Keyword(s):  
Neural Network ◽  
Infectious Disease ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Comprehensive Evaluation Model

Application of Genetic BP Neural Network in Safety Comprehensive Evaluation of Tailing

2014 ◽  
Vol 687-691 ◽  
pp. 2402-2406
Author(s):  
Song Jiang ◽  
Hui Wen He ◽  
Hong Bo Liu ◽  
Kang Ting Lv
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Threshold Value ◽  
Generalization Ability ◽  
Learning Speed ◽  
Security Levels ◽  
Improved Accuracy

Based on safety assessment factors determined by operation characteristics of a certain tailing ,genetic BP neural network evaluation model is established. To overcome such problems of BP neural network as slow convergence ,poor generalization ability and easy to fall into local minimum value,this paper proposes to use genetic algorithm to optimize threshold value,weights and structure of neural network. Thus,by taking advantage of extensive mapping ability of neural network and global search ability of genetic algorithm,neural network and genetic algorithm will have complementary advantages and the learning speed of network will be accelerated. The application of the described method shows optimized fitting precision,improved accuracy and efficiency ,and enhanced generalization ability of BP neural network. In conclusion,this model can effectively reflect and accurately evaluate non-linear relations between security levels and evaluation factors in tailing.

Intelligent teaching ability of contemporary college talents based on BP neural network and fuzzy mathematical model

2020 ◽  
Vol 39 (4) ◽  
pp. 4913-4923
Author(s):  
Han He ◽  
Hongcui Yan ◽  
Weiwei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Performance Comparison ◽  
Fuzzy Similarity ◽  
Model Combining ◽  
Comparison Results ◽  
Combining Data ◽  
Teaching Ability

In the evaluation of traditional college talents’ teaching ability, the importance of evaluation indicators lacks evaluation, and the evaluation results are relatively random. In order to improve the evaluation efficiency of university scientific research talents, this study combines BP neural network and fuzzy mathematical theory to build an evaluation model. Combining the talent training process and ability requirements of colleges and universities, a secondary index system is proposed, and the weight of the evaluation index is determined by combining data collection. This paper first normalizes the samples, determines the training and test samples, and then uses trial and error to determine the number of hidden layer neurons. Then use fuzzy mathematics theory to construct fuzzy similarity matrix to describe the fuzzy relationship between factor domain and judgement domain. Calculate membership to get comprehensive evaluation results. Finally, this paper uses statistical methods to draw the results into statistical charts and combines the simulation results to obtain performance comparison results. The feasibility of the model is verified by experimental research, and the model can be applied to practice, and can provide theoretical reference for subsequent related research.

Comprehensive Evaluation of Implementation Effect on Later-Period Supportive Policy of Reservoir Resettlement Based on ANFIS

2014 ◽  
Vol 912-914 ◽  
pp. 1874-1878 ◽  
Author(s):  
Xin Xu ◽  
Xiao Yi Wang ◽  
Zhao Yang Wang ◽  
Yu Hang Long ◽  
Suo Jun Xu
Keyword(s):  
Neural Network ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Nonlinear Characteristics ◽  
Comprehensive Evaluation Model ◽  
Implementation Effect ◽  
Reservoir Resettlement

According to engineering features of later-period supportive policy on reservoir resettlement, Economic evaluation index system of implementation effect on later-period supportive policy of reservoir resettlement is built to assess the implementation result of reservoir resettlement policy effectively in recent years. Considering the complexity of the evaluation index and the nonlinear characteristics of evaluation process, it is built that the comprehensive evaluation model of implementation effect on later-period supportive policy of reservoir resettlement based on ANFIS(Fuzzy artificial neural network) to provide the decision-making reference for implementation and improvement of later-period supportive policy of reservoir resettlement.

scholarly journals Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinglong Kan ◽  
Lin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Influencing Factors ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Analysis And Evaluation ◽  
The Neural Network ◽  
Comprehensive Evaluation Model ◽  
Evolutionary Genetic ◽  
Tourism Resources

With the development of neural network technology and the rapid growth of China’s tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm and designs the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm. The collection and acquisition of data information are realized from the aspects of resource income status, tourism development investment, and sustainability evaluation in the tourism area. The multispecies evolutionary genetic algorithm is used for comprehensive analysis and evaluation. The algorithm can realize the complex analysis and comprehensive evaluation of the core influencing factors of neural network. Accurate analysis and evaluation were carried out according to the different characteristics of tourism resources and the current situation of tourism income. The results show that the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm has the advantages of high practicability, good sorting effect of variable ratio, and good data integration. It can effectively analyze and compare the comprehensive evaluation factors affecting tourism resources in different ratios.

Intelligent Lighting Control System in Large-Scale Sports Competition Venues

2018 ◽  
pp. 172-182 ◽  
Author(s):  
Shengmin CAO
Keyword(s):  
Control System ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Practical Significance ◽  
Lighting Control ◽  
Sports Events ◽  
Planning And Design ◽  
Comprehensive Evaluation Model

This paper mainly studies the application of intelligent lighting control system in different sports events in large sports competition venues. We take the Xiantao Stadium, a large­scale sports competition venue in Zaozhuang City, Shandong Province as an example, to study its intelligent lighting control system. In this paper, the PID (proportion – integral – derivative) incremental control model and the Karatsuba multiplication model are used, and the intelligent lighting control system is designed and implemented by multi­level fuzzy comprehensive evaluation model. Finally, the paper evaluates the actual effect of the intelligent lighting control system. The research shows that the intelligent lighting control system designed in this paper can accurately control the lighting of different sports in large stadiums. The research in this paper has important practical significance for the planning and design of large­scale sports competition venues.

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