Probability integral parameter prediction method for optimizing BP neural network based on MIV-GP algorithm

Abstract

According to the invention, a probability integral method parameter prediction model for optimizing the BP neural network based on a combined algorithm (GP) of a genetic algorithm and a particle swarmoptimization algorithm, and an input layer of the BP neural network is optimized by adopting an average influence value algorithm (MIV), so that the complexity of the network is reduced, and the purpose of improving the prediction precision is achieved. An MIV-GP-BP model is established by taking actual measurement data of 50 working surfaces as a training set and a test set of the BP neural network, and the precision and reliability of a model prediction result are analyzed; the results show that: in five parameters, the root mean square error ranges from 0.0058 to 1.1575; the maximum relative error of q, tan beta, b and theta is not more than 5.42%, the average relative median error is less than 2.81%, the s / H relative error is not more than 9.66%, the average relative median error is less than 4.31% (the parameter itself is small), and the optimized neural network model has higher prediction precision and stability.

Description

technical field [0001] The invention relates to the field of probability integral parameter prediction, in particular to a probability integral parameter prediction method based on MIV-GP algorithm optimization BP neural network. Background technique [0002] With the rapid development of China's economy, the demand for coal resources is also increasing. In recent years, a large number of underground coal resources have been mined out. Underground coal mining has caused a series of environmental problems, such as: surface subsidence, cracks, dust, solid waste, etc., posing a serious threat to the production and life of the mining area. In order to maximize the extraction of coal resources and reduce large-scale surface subsidence, scholars have conducted extensive research on mining subsidence prediction theory. Among them, the probability integral method based on the theory of stochastic medium mechanics is a widely used mining subsidence prediction method, so the acquisit...

AI Technical Summary

Problems solved by technology

These algorithms include simulated annealing (SA), GA algorithm, PSO algorithm, etc. Among these optimization algorithms, PSO algorithm is simple and has high operation efficiency, but it has the disadvantages of low precision and easy to fall into local optimum.

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