Analysis of Prediction Model of Failure Depth of Mine Floor Based on Fuzzy Neural Network

2018 ◽  
Vol 37 (1) ◽  
pp. 71-76 ◽  
Author(s):  
Zhongchang Wang ◽  
Wenting Zhao ◽  
Xin Hu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Failure Depth ◽  
Mine Floor

Surface Roughness Prediction Model of Cylinder Grinding

2012 ◽  
Vol 157-158 ◽  
pp. 123-126 ◽  
Author(s):  
Ning Ding ◽  
Yi Chen Wang ◽  
Ding Tong Zhang ◽  
Yu Xiang Shi ◽  
Jian Shi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Vibration Signal ◽  
Feed Speed ◽  
Vibration Data ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction

Based on the theory of roughness during cylinder grinding and the theory of fuzzy-neural network, a surface roughness intelligent prediction model is developed in this paper. The feed, speed, and the vibration data are the inputs for the model. An accelerometer is used to gather the vibration signal in real time. The model is used in the grinding experiment, and verifies the feasibility of the proposed model.

Fuzzy neural network based prediction model applied in primary component analysis

2017 ◽  
Vol 20 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Xiaohong Peng ◽  
Shiyi Xie ◽  
Yinghuai Yu ◽  
Zhenlu Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Component Analysis ◽  
Primary Component ◽  
Fuzzy Neural

Closed Loop Dynamic Fuzzy Neural Network for Real-Time Lifetime Forecasting

2013 ◽  
Vol 834-836 ◽  
pp. 1074-1080
Author(s):  
Wen Wang Li ◽  
Gao Feng Zheng ◽  
Jian Yi Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Real Time ◽  
Fuzzy Neural Network ◽  
Closed Loop ◽  
Failure Time ◽  
Membrane Function ◽  
The Real ◽  
Fuzzy Neural ◽  
Monitoring Information

Real-time lifetime forecasting has extensive application in the fields of machine system manufacturing and integration, which is a good way to promote the dependability and operation stability. In this paper, a closed loop adaptive forecasting model with feedback channel of state monitoring information is built up for the real-time lifetime forecasting. The difference of working state between prediction and monitoring information is used to evaluate the prediction performance. The dynamic fuzzy neural network introduced into the prediction model, in which the fuzzy rule, membrane function and structure parameters can be adjusted according to the evaluate results. A service lifetime testing experiment of gear case is utilized to validate the prediction model. The proposed model achieved reasonable precision with an error of less than 1 hour between the failure time of experimental results and the forecasting remaining lifetime. The adaptive prediction method can deal with the real-time lifetime forecasting for multiple factors and nonlinear system without specific parameters structure.

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