基于粒子群优化的PLS-LCF岩爆灾害预测模型研究

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摘要岩爆灾害的预测是一个复杂的系统性问题，有必要全面综合考虑影响岩爆发生的各种因素，但当考虑变量较多时，变量间存在的多重共线性会影响分析的客观性。为了消除这种不利影响，更好地预测岩爆灾害，利用偏最小二乘法对影响岩爆的因素进行分析，提取一个对因变量解释性最强的新综合变量，较好地克服了变量间的多重共线性，并采用逻辑曲线函数表达新成分与岩爆等级之间的非线性关系，通过具有全局寻优性能的粒子群算法对函数参数优化求解，进而建立岩爆灾害预测的粒子群优化PLS-LCF模型。对模型所进行的测试显示其良好的精度，将其应用到实际工程的岩爆预测当中，分析结果与实际情况吻合较好，表明该模型在岩爆灾害预测中具有可行性与有效性。

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关键词 ：岩石力学, 岩爆预测, 多重共线性, 偏最小二乘回归, 粒子群, 逻辑曲线函数

Abstract：The prediction of rock burst is a complex systemic problem. It is necessary to consider the various factors affecting the rock burst comprehensively. But when variables to be considered are very large，the multicollinearity among variables will affect the objectivity of the analysis. In order to eliminate the adverse effect and effectively predict rock burst，the affecting factors for rock burst are analyzed by means of partial least-squares regression(PLSR). A new synthesis variable with better interpretation to the dependent variable is extracted and it can preferably overcome the multicollinearity among variables. The nonlinear relationship between composition values and rock burst grades is established according to the logistic curve function(LCF). The particle swarm optimization(PSO) with the global optimization is used to optimize the parameters of LCF. So far，a PLS-LCF prediction model of rock burst based on PSO is built. The test results of the model show a very good precision. The evaluation results obtained by applying the developed model to practical engineering are well consistent with the practical situation，which indicates that the model is feasible and effective for rock burst prediction.

Key words： rock mechanics rock burst prediction multicollinearity partial least-squares regression particle swarm optimization logistic curve function

收稿日期: 2012-08-13

引用本文:

言志信1，2，贺香2，龚斌2. 基于粒子群优化的PLS-LCF岩爆灾害预测模型研究[J]. 岩石力学与工程学报, 2013, 32(s2): 3180-3186.
YAN Zhixin1，2，HE Xiang2，GONG Bin2. STUDY OF PLS-LCF MODEL FOR ROCK BURST PREDICTION BASED ON PARTICLE SWARM OPTIMIZATION. , 2013, 32(s2): 3180-3186.

链接本文:

https://rockmech.whrsm.ac.cn/CN/Y2013/V32/Is2/3180

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