Weapon systematic safety evaluation model based on genetic algorithm and BP neural network

2011 ◽  
Author(s):  
Cheng Kai ◽  
Zhang Hong-jun ◽  
Xu Bo ◽  
Shan Li-li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Model Based

Research on the Safety Evaluation of Large Recreation Facilities Based on BP Neural Network

2014 ◽  
Vol 522-524 ◽  
pp. 881-886
Author(s):  
Yu Zhang ◽  
Zhi Rong Wang ◽  
Qing Qing Zuo ◽  
Xin Dong Zhang ◽  
Xiang Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Human Factor ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Qualitative Evaluation ◽  
Evaluation Index System ◽  
Model Based ◽  
Network Training ◽  
Recreation Facilities

A safety evaluation index system regarding to the current safety situation of large recreation facilities in China is established. 13 secondary standard items are built by considerring human factor, equipment factor, environment factor and management factor. The existing safety evaluation of large recreation facilities are conducted by qualitative evaluation methods with highly fuzziness. The evaluation results are uncertain. After the network training, a safety evaluation model based on BP neural network is built. It can reduce the subjectivity of qualitative evaluation effectively with more scientific and objective results. Through the model based on BP neural network, the present safety situation of one large amusement facility is evaluated. The evaluation result is consistent with the actual situation. The method based on BP neural network in the paper provides a new method for safety evaluation of large recreation facilities.

The Coal Mine Flooding Emergency Ability Evaluation Based on GA-WNN

2012 ◽  
Vol 452-453 ◽  
pp. 782-788
Author(s):  
Jin Feng Wang ◽  
Li Jie Feng ◽  
Zhao Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Evaluation Model ◽  
Wavelet Neural Network ◽  
Time Frequency ◽  
Model Based ◽  
Time Frequency Localization ◽  
Mine Flooding ◽  
Ability Evaluation

For the coal resources working which are affected by the coal mine flooding seriously, this paper make an analysis on the factors which affect the coal mine flooding emergency ability evaluation model based on GA-WNN is established through the wavelet neural network value which is optimized with genetic algorithm. This model combined the global optimization ability of genetic algorithm with the time-frequency localization of wavelet neural network. This combination can make up for many defects (for example, the neural network structure should be given artificially, the function can got local minimum easily and so on). Therefore, the local mine flooding emergency ability evaluation model based on genetic algorithm and wavelet neural network have higher reliability and calculation ability, and is beneficial to the pre-control management for coal mine flooding rescue.

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.

scholarly journals An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

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.

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