分层鱼群优化支持向量机预测巷道围岩松动圈厚度

为了及时掌握巷道围岩松动圈的厚度,以便采取措施控制围岩的安全性,采用基于最大间隔算法的支持向量机进行预测。考虑支持向量机的性能很大程度依赖于参数的选择,提出改进的人工鱼群算法优化支持向量机的参数,以取得更好的准确度。首先对基本人工鱼群算法增加了种类分层和交叉变异,然后以此优化的参数对考查数据进行支持向量机回归预测。通过人工鱼群行为和参数的改进,扩大了搜索空间,增加了全局优化的稳定性,克服了人工鱼群后期寻优速度慢等问题。对某巷道围岩松动圈厚度监测与预测结果表明:该模型的预测精度较高,缩短了寻找参数的时间,泛化性能提高,收敛加快,可以有效地指导巷道围岩安全性的监测。

Support Vector Machine (SVM) Prediction of Roadway Surrounding Rock Loose Circle Thickness Optimized by Layered Fish

To timely obtain the thickness of the roadway surrounding rock loose circle and take measures to ensure the safety of the surrounding rock，support vector machine (SVM) based on maximum interval algorithm is adopted to make the prediction.Considering that the performance of SVM largely depends on the choice of parameters，the improved artificial fish algorithm to optimize the parameters of SVM is put forward，in order to obtain better accuracy.This method firstly added the idea of layering and crossover mutation to the basic artificial fish algorithm，then optimized the parameters and adopted the SVM to regress and predict for the test data.By improvement of artificial fish behavior and the parameters，the search space is expanded and the stability of global optimization is increased.Problems existing in the artificial fish late optimization such as slow speed are solved.The prediction on monitoring data of a roadway surrounding rock loose circle thickness showed that the model of roadway surrounding rock loose circle thickness has a higher prediction accuracy，which can shorten the search time，increase convergence speed and improve its generalization performance.This research can play a guide role in monitoring the safety of surrounding rock of roadway.