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Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine 被引量:11

Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine
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摘要 A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares(FLS)-support vector machine(SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high. A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares (FLS)-support vector machine (SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.
出处 《Journal of Central South University》 SCIE EI CAS 2014年第3期1085-1090,共6页 中南大学学报(英文版)
基金 Project(2012BAK09B02-05) supported by the National Key Technology R&D Program of China during the Twelfth Five-year Period Project(51274250) supported by the National Natural Science Foundation of China
关键词 最小二乘支持向量机 土壤参数 振动钻削 岩石 模糊 金属矿 自适应遗传算法 自由度模型 rock and soil fuzzy theory vibration excavation least squares-support vector machine identification
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