Information fusion technology based on T-S Fuzzy neural network for coal mine gas prediction

2012 ◽  
Author(s):  
Jinpeng Li ◽  
Li Gao
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Coal Mine ◽  
Fuzzy Neural Network ◽  
Fusion Technology ◽  
Coal Mine Gas ◽  
Fuzzy Neural ◽  
Mine Gas

Intelligent Multisensor State Information Fusion with a Stochastic, Fuzzy, Neural Network

1999 ◽  
Vol 32 (2) ◽  
pp. 5243-5248
Author(s):  
Zhongliang Jing ◽  
Albert C.J. Luo ◽  
Masayoshi Toraizuka ◽  
Huahua Yan ◽  
Guanzhong Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Fuzzy Neural Network ◽  
State Information ◽  
Fuzzy Neural

Condenser Fault Diagnosis Based on FNN and Data Fusion

2010 ◽  
Vol 44-47 ◽  
pp. 3762-3766 ◽  
Author(s):  
Fei Xia ◽  
Hao Zhang ◽  
Dao Gang Peng ◽  
Hui Li ◽  
Yi Kang Su
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Membership Functions ◽  
Fusion Technology ◽  
New Approach ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
General Method

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

scholarly journals Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meng Wang ◽  
Caiwang Tai ◽  
Qiaofeng Zhang ◽  
Zongwei Yang ◽  
Jiazheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Fuzzy Neural Network ◽  
Engineering Practice ◽  
Global Approximation ◽  
Working Face ◽  
Fuzzy Neural ◽  
Classification Evaluation ◽  
Seam Mining ◽  
Longwall Top Coal Caving

AbstractLongwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.

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