Method for solving probability integral parameters on basis of invasive weed optimization algorithm

Abstract

The invention discloses a method for solving probability integral parameters on the basis of an invasive weed optimization algorithm. The method comprises the following steps of (1) substituting the geological mining condition parameter and the working face population initial parameter of a known working face region into a probability integral formula, and predicting the subsidence value and the horizontal moving value of an observation point; (2) building an error function by solving the difference value between the practically measured subsidence value and the horizontal moving value of theworking face observation point and the estimated subsidence value and the horizontal moving value in the step 1; and (3) using the error function in the step 2 as a fitness function; using the invasive weed optimization algorithm for inverting the probability integral parameters; and solving the probability integral parameters of the working face. The method provided by the invention is simple; the invasive weed optimization algorithm is applied to the solution of the probability integral parameters for the first time; all of the probability integral parameters can be solved; and the advantageof high parameter solving precision is realized.

Description

technical field [0001] The invention relates to the technical field of mining subsidence prediction parameters, in particular to a method for obtaining probability integral parameters based on an invasive weed optimization algorithm. Background technique [0002] With the increase of the country's demand for coal resources, it is of great significance to mine a large amount of coal resources under buildings, railways, and water bodies (referred to as "three under"). Mining subsidence caused by coal mining destroys buildings and structures on the surface, thus affecting human production and life. In order to minimize the loss caused by mining subsidence, it is necessary to make an accurate prediction of the subsidence that will occur during the mining process before mining. The method commonly used to predict mining subsidence is the probability integral method, so how to determine the parameters of the probability integral becomes the key to improving the prediction accurac...

AI Technical Summary

Problems solved by technology

Traditional optimization algorithms such as the modulus-vector method have high precision in finding parameters, but they have high requirements for the initial value of the parameters, and the solution process tends to diverge and easily fall into local optimal solutions; intelligent optimization algorithms and improved algorithms, such as genetic algorithms, can It is a good solution to the problems caused by traditional optimization algorithms, but the calculation process needs to encode parameters, which makes the programming complicated, and at the same time, the random search of the previous generation population is easy to lead to premature phenomenon

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