基于Adaboost-SVM组合算法的爆破振动强度预测研究

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摘要为了提高爆破振动强度预测精度，提出了基于Adaboost-SVM组合算法的爆破振动强度预测方法。采用主分量分析法，从7种爆破振动强度影响因素中确定了3类主要因素，并建立训练样本集，选用高斯径向基核函数建立SVM预测模型，经过对模型参数不断训练和优化调整，实现了对爆破振动强度的预测，最后通过Adaboost-SVM组合算法构建预测模型，进一步提升了预测精度。结果表明，SVM模型在预测精度上高于传统经验公式法和BP神经网络法，且训练速度更快；而提出的Adaboost-SVM组合算法能够进一步将预测精度提高至97%以上。

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	梅比1
	汪旭光2
	杨仁树1

关键词 ：爆破振动, 预测, Adaboost, 主分量分析, 支持向量机

Abstract：In order to improve the prediction accuracy of blasting vibration intensity, a prediction method of blasting vibration intensity based on Adaboost-SVM combined algorithm was proposed.By using the principal component analysis method, three main factors were determined from seven kinds of influencing factors of blasting vibration intensity, and the training sample set was established.The SVM prediction model was established by selecting the Gaussian radial basis kernel function.The model parameters were continuously trained and optimized,thus the prediction of blasting vibration intensity was realized.Finally, the prediction model was constructed by Adaboost-SVM combined algorithm, and the prediction accuracy was further improved.It was shown that the prediction accuracy of SVM model is higher than that of traditional empirical formula method and BP neural network method, and the training speed is faster.The proposed Adaboost-SVM combination algorithm can further improve the prediction accuracy to above 97%.

Key words： blasting vibration prediction Adaboost Principal Component Analysis Support Vector Machine

收稿日期: 2018-10-16 出版日期: 2019-09-15

引用本文:

梅比1，汪旭光2，杨仁树1. 基于Adaboost-SVM组合算法的爆破振动强度预测研究[J]. 振动与冲击, 2019, 38(18): 231-235.
MEI Bi1，WANG Xuguang2，YANG Renshu1. Prediction of blasting vibration intensity based on Adaboost-SVM combination algorithm. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(18): 231-235.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I18/231

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