Application of Gray Elman Neural Network to Predict the Gas Emission Amount

The prediction of mine Gas Emission Amount is an important part of helping to make rational gas control measures. In order to improve the accuracy of mine gas emission prediction, this paper introduced the grey theory into the Elman artificial neural network theory, and combined the gray prediction model GM (1,1) with the Elman neural network model,established a gray Elman artificial neural network prediction model of gas emission, and carried on the simulation through software Matlab. Practice and experiment showed that this method compared well, and is superior to the traditional Grey prediction model, moreover this method also applied to the situation of original data was few or the historical data had transition. The forecasting results from this method can be more reliable and accurate, so it can instruct the practice accurately

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Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

Mathematical Problems in Engineering ◽

10.1155/2014/918307 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Yueru Ma ◽

Lijun Peng

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Coupled Model ◽

Labor Turnover ◽

Grey Theory ◽

Random Disturbance ◽

Grey Prediction ◽

Ann Model ◽

Model Approximation ◽

Artificial Neural

The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN). A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

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Plant growth prediction model for lettuce (Lactuca sativa.) in plant factories using artificial neural network

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/733/1/012027 ◽

2021 ◽

Vol 733 (1) ◽

pp. 012027

Author(s):

A Rizkiana ◽

A P Nugroho ◽

N M Salma ◽

S Afif ◽

R E Masithoh ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Plant Growth ◽

Prediction Model ◽

Lactuca Sativa ◽

Growth Prediction ◽

Artificial Neural

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Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

Shock and Vibration ◽

10.1155/2015/106945 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Yasir Hassan Ali ◽

Roslan Abd Rahman ◽

Raja Ishak Raja Hamzah

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Film Thickness ◽

Spur Gear ◽

Back Propagation Algorithm ◽

Elman Neural Network ◽

Feed Forward ◽

Oil Film ◽

Feed Forward Network ◽

Artificial Neural

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

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