基于IWOA-HKELM的矿井突水水源识别

Identification method of mine water inrush source based on IWOA-HKELM

为提高矿井突水水源识别的精度,提出一种改进鲸鱼优化算法(IWOA)-混合核极限学习机(HKELM)的水源识别模型。首先将高斯核函数和多项式核函数相结合,构造学习能力和泛化性能较好的HKELM;然后针对鲸鱼优化算法(WOA)易陷入局部最优的问题,提出IWOA算法,引入帐篷映射、改进非线性因子以及设置反向精英学习阈值等3种策略来降低算法过早收敛的概率,并得到更优结果;最后将新庄孜矿的突水水源资料作为仿真数据,降维处理后输入到IWOA-HKELM模型中结果预测。研究表明:通过IWOA优化HKELM参数,可提高HKELM的整体预测性能;IWOA-HKELM的预测结果与实际情况完全一致,与其他模型相比,该模型的平均分类准确率明显提高,平均均方误差和分类准确率标准差明显降低。

In order to improve accuracy in identifying mine water inrush source,an IWOA-HKELM water source identification model is proposed.Firstly,HKELM,featuring better learning ability and generalization performance,was constructed on combined basis of Gaussian kernel function and polynomial kernel function.Secondly,IWOA algorithm was proposed considering that WOA was easy to fall into local optimization.Then,three strategies were introduced to reduce probability of premature convergence and obtain better results,including tent mapping,improvement of non-linear factor and setting of reverse elite learning threshold.Finally,water inrush source data of Xinzhuangzi Mine,being taken as simulation data,was put in IWOA-HKELM model for result prediction after dimension reduction.The results show that optimization of HKELM parameters through IWOA can improve the algorithm's overall prediction performance.Prediction results of IWOA-HKELM are completely consistent with actual situation.Compared with other models,the proposed model obviously excels in terms of average classification accuracy with its average mean square error and standard deviation of classification accuracy being significantly reduced.