Ore rock intensity prediction method based on component thermodynamic gene expression programming

Abstract

The invention relates to an ore rock intensity prediction method based on component thermodynamic gene expression programming. With a provided component thermodynamic gene expression programming algorithm, the invention adopts a water absorption rate, a dry density, wave impedance, a dynamic Poisson's ratio, and a dynamic elastic modulus as input variables, adopts compressive strength as an output variable, automatically and intelligently digs out a mathematical model of the ore rock intensity, and thus predicts the compressive strength of the ore rock. The invention can quantitatively harmonize the balance between a selected pressure and population diversity in the gene expression programming, and thus improves the rate of convergence, solving precision and algorithmic stability of traditional gene expression programming when applied to the prediction of ore rock intensity.

Description

technical field [0001] The invention relates to a method for predicting ore-rock strength, in particular to a method for predicting ore-rock strength based on component thermodynamic gene expression programming. Background technique [0002] The strength of ore and rock is a basic problem in mining engineering, but because the traditional method of predicting the strength of ore and rock requires a large number of field tests, it leads to a sharp increase in manpower and funds. In actual engineering practice, due to the constraints of funds, equipment and other conditions, it is often impossible to carry out a large number of field tests. Therefore, how to predict the strength of ore rock efficiently and accurately is an important issue that many engineers pay close attention to. At present, there are many prediction methods for ore rock strength, which can be mainly divided into: mathematical and physical prediction methods and intelligent prediction methods. Mathematical...

AI Technical Summary

Problems solved by technology

Under normal circumstances, when the selection pressure is too large, the more individuals in the population that are close to the current optimal individual, the better the average fitness value of the population, which can speed up the convergence of the genetic expression programming algorithm, but it will lead to Most of the individuals in the population tend to be close to the current optimal individual, and the diversity of the population becomes worse, which increases the probability of the algorithm falling into a local optimum; when the selection pressure is too small, although the distribution of individuals in the population can be dispersed, the population diversity become better and increase the probability of the algorithm converging to the global optimal solution, but this will slow down the convergence speed of the gene expression programming algorithm

At present, the research results on how to quantitatively coordinate the balance between the selection pressure and population diversity of the gene expression programming algorithm are still lacking.

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