Reliability analysis of elastic link mechanism based on BP Neural Network

2012 ◽  
Author(s):  
Jian Xiao ◽  
Liping He ◽  
Hong-Zhong Huang ◽  
Xiaoling Zhang ◽  
Zhonglai Wang
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Bp Neural Network ◽  
Link Mechanism ◽  
Elastic Link

scholarly journals Motion Reliability Analysis of Unlocking Trigger Device Based on CPSO-BR-BP Neural Network with Uncertain Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yun Tian ◽  
Hongtao Fan ◽  
Yuliang Zhang ◽  
Licheng Liu ◽  
Kang Gong
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Particle Swarm ◽  
Separation Process ◽  
Structural Function ◽  
Particle Swarm Algorithm ◽  
Computationally Efficient ◽  
Proxy Model

Aiming at overcoming the problem that the mechanism function of the unlocking trigger device is difficult to obtain and the corresponding reliability analysis cannot be performed, a motion reliability analysis method based on the CPSO-BR-BP neural network proxy model is proposed. Firstly, the particle swarm algorithm is optimized through the chaotic sequence, and the back-propagation (BP) neural network is optimized using Chaos Particle Swarm Optimization (CPSO) and Bayesian Regularization (BR) algorithm. The CPSO-BR-BP neural network proxy model is established, and the reliability of shape memory alloys (SMA) wire unlocking based on the structural function is calculated. Moreover, according to the structural function of the separation process, the motion reliability based on the proxy model and the improved membership function is calculated. Finally, a series reliability model is established based on the unlocking process and the separation process to calculate the reliability of the whole machine. The reliability of the unlocking trigger device is analyzed by the proposed method. Results show that the proposed method is computationally efficient with the calculated reliability of 0.9987.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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