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Method for porecasting water upwelling amount of mine well based on nerve network model

A neural network model and prediction method technology, which is applied in the field of prediction of underground water inflow in coal mines, can solve the problems of unsatisfactory network promotion performance, difficulty in achieving accurate prediction, and failure to give full play to the advantages of the intelligent prediction method of the neural network model. The effect of improving generalization performance, accurate development trend, and good learning accuracy

Inactive Publication Date: 2006-06-14
孟江
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Problems solved by technology

The above method shows good properties in network learning, and its convergence speed has been significantly improved, but its performance in network promotion (generalization) is not satisfactory, it is difficult to accurately predict the development of future trends, and it has not fully utilized the neural network Advantages of the Smart Predictive Approach to Network Models

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  • Method for porecasting water upwelling amount of mine well based on nerve network model
  • Method for porecasting water upwelling amount of mine well based on nerve network model
  • Method for porecasting water upwelling amount of mine well based on nerve network model

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Embodiment Construction

[0029] The technical scheme of the present invention will be further described below in conjunction with the accompanying drawings.

[0030] As can be seen from Fig. 1, the neural network model of the present invention needs to go through the following steps when predicting the mine water inflow:

[0031] 1. Obtain the historical data of mine water inflow U i and converted to a time series sample pair

[0032] Firstly, the water inflow data U i (i=1, 2, ..., T) perform linear transformation, and normalize the data through the maximum and minimum values, namely:

[0033] V i =(U i -U min ) / (U max -U min )

[0034] Among them, U min and U max Respectively U i The minimum and maximum values ​​of V; V i is the normalized water inflow data; T is the number of water inflow data.

[0035] Later on V i Carry out time series transformation, according to the difference of the quasi-periodical performance of the forecast data, perform time series t...

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Abstract

The invention discloses mine inflow prediction method based on neural network model. It includes the following steps: obtaining mine inflow historical data and transform to time series type; setting up BP neural network prediction and learning method model; training prediction model; detecting and output. Its features are that the learning method model is generalization; and it is used to train the prediction model. Its advantages are high learning efficiency, good generalization, and exact forecast. It can exactly forecast future developing trend and grasp the current data developing trend.

Description

technical field [0001] The invention relates to the prediction of underground water inflow in coal mines, in particular the prediction of mine water inflow based on neural network. Background technique [0002] It is an important and complicated task to correctly predict mine water inflow. It is the fundamental task of mine hydrogeological investigation and an important index for technical and economic evaluation of coal fields. Commonly used water inflow forecasting methods mainly include statistical forecasting based on time series analysis, gray forecasting based on gray theory, and intelligent forecasting technology represented by neural network models. Since the dynamic process of the mine groundwater system is controlled not only by fluctuations induced by internal structural changes, but also by the external environment, there is a high degree of nonlinearity, randomness and complexity, and the explicit expression is used to describe the relationship between the predi...

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Application Information

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IPC IPC(8): E21B47/10G06F19/00
Inventor 孟江安坤
Owner 孟江
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