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Soft-sensing method for rock strength based on adaptive artificial bee colony optimization

An artificial bee colony optimization and adaptability technology, applied in the field of rock strength measurement, can solve the problem of low soft measurement accuracy

Active Publication Date: 2018-10-16
JIANGXI UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to propose an adaptive artificial bee colony optimization soft-sensing method for ore-rock strength, aiming at the shortcomings of the traditional neural network model, which is easy to fall into a local optimum when soft-sensing the strength of ore and rock, and the accuracy of the soft-sensing is not high.

Method used

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  • Soft-sensing method for rock strength based on adaptive artificial bee colony optimization
  • Soft-sensing method for rock strength based on adaptive artificial bee colony optimization
  • Soft-sensing method for rock strength based on adaptive artificial bee colony optimization

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Experimental program
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Embodiment

[0066] Step 1, determine the area where the strength of the ore rock needs to be softly measured, then collect RN=28 ore rock test pieces in the determined area, and carry out experiments on the ore rock test pieces to measure the water absorption and dryness of each ore rock test piece. Density, wave impedance, dynamic Poisson's ratio, dynamic elastic modulus, and compressive strength, the experimental data of the rock specimen is used as a sample data set; then the collected sample data set is normalized;

[0067] Step 2, the user initializes the parameters, and the initialization parameters include the number of hidden layer neurons of the three-layer perceptron neural network HN=6, the population size Popsize=50, the maximum number of times of unimproved Limit=100, and the neighborhood radius NK=5 , the maximum number of evaluations MAX_FEs=300000;

[0068] Step 3, the current evolution algebra t=0, the current evaluation times FEs=0;

[0069] Step 4, let the input variab...

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Abstract

The invention discloses an ore rock strength soft sensing method based on adaptive artificial bee colony optimization. The method of the invention adopts a three-layer perception neural network as a soft sensing model for ore rock strength, and utilizes the adaptive artificial bee colony algorithm to optimize a connection weight and an offset value of the designed neural network. In the adaptive artificial bee colony algorithm, search scaling factors are generated adaptively according to the feedback information of adaptive values, and a Gaussian mutation strategy based on neighbor optimal individuals and global optimal individuals is designed to adaptively generate new individuals. The method of the invention can raise the precision of soft sensing of the strength of ore rocks, and raises the efficiency of measuring the strength of the ore rocks.

Description

technical field [0001] The invention relates to the field of rock strength measurement, in particular to an adaptive artificial bee colony optimization soft-measurement method for rock strength. Background technique [0002] In mining engineering, in order to ensure the safety of mine production, it is often necessary to master the strength of ore rock. Therefore, the measurement of ore rock strength is an important basic work in mine production practice. The traditional method of measuring the strength of ore and rock often needs to spend a lot of manpower and material resources, which leads to the inefficiency of measuring the strength of ore and rock. Therefore, how to quickly and accurately measure the strength of ore rocks has always been a subject of continuous research by mining engineers. In order to improve the efficiency of rock strength measurement, many engineers put forward the soft measurement method of rock strength. The measurement method establishes a mat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/08G06N3/04G06Q50/02
CPCG06N3/04G06N3/086G06Q50/02
Inventor 郭肇禄杨火根周才英刘小生尹宝勇刘松华邹玮刚
Owner JIANGXI UNIV OF SCI & TECH
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