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A pressure control method for emulsion pump station based on Elman neural network.

Tan C, Qi N, Zhou X, Liu X, Yao X, Wang Z, Si L - Comput Intell Neurosci (2015)

Bottom Line: In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed.The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented.Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

View Article: PubMed Central - PubMed

Affiliation: School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.

ABSTRACT
In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

Show MeSH
Principle of Elman neural network.
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Related In: Results  -  Collection


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fig2: Principle of Elman neural network.

Mentions: Neural network is an ideal type of nonlinear approximator. The important components are input layer, hidden layer, connected layer, and output layer. Compared with BP neural network, connected layer is added for partial feedback in Elman neural network [29]. Transfer function of it is linear, and to remember the state of past time series delay units are included. Having both memory unit and input of network as input of hidden layer, we can see dynamic memory function [30] in Elman neural network. Transfer function of connected and output layer is linear, while that of hidden layer is nonlinear, such as hyperbolic S nonlinear function, step function, and prelinear function. Because of hidden layer receiving the data from input and memory data in connected layer, the same input can bring different output. The principle of Elman neural network with the characteristic of multi-input and single-output is shown in Figure 2.


A pressure control method for emulsion pump station based on Elman neural network.

Tan C, Qi N, Zhou X, Liu X, Yao X, Wang Z, Si L - Comput Intell Neurosci (2015)

Principle of Elman neural network.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4378604&req=5

fig2: Principle of Elman neural network.
Mentions: Neural network is an ideal type of nonlinear approximator. The important components are input layer, hidden layer, connected layer, and output layer. Compared with BP neural network, connected layer is added for partial feedback in Elman neural network [29]. Transfer function of it is linear, and to remember the state of past time series delay units are included. Having both memory unit and input of network as input of hidden layer, we can see dynamic memory function [30] in Elman neural network. Transfer function of connected and output layer is linear, while that of hidden layer is nonlinear, such as hyperbolic S nonlinear function, step function, and prelinear function. Because of hidden layer receiving the data from input and memory data in connected layer, the same input can bring different output. The principle of Elman neural network with the characteristic of multi-input and single-output is shown in Figure 2.

Bottom Line: In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed.The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented.Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

View Article: PubMed Central - PubMed

Affiliation: School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.

ABSTRACT
In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

Show MeSH