基于FIG和GWO-SVM的灌浆功率时序预测

为更好地预测灌浆功率时序，建立基于模糊信息粒化(FIG)和灰狼优化支持向量机(GWO-SVM)的灌浆功率时序预测模型。首先，引入信息粒计算方法，将原始详尽的时间序列数值点分解为一系列信息粒，以减少模型的数据输入总量；其次，基于模糊集理论，采用模糊集算子对每个信息粒进行模糊计算，使得到的模糊信息粒可以合理地表示原始数值点集；最后，以支持向量机作为预测工具，并采用灰狼优化算法进行参数寻优，对产生的模糊信息粒进行快速准确的预测。结合实际工程，应用该预测模型对灌浆功率的波动范围和变化趋势进行预测研究，经过性能评价和对比分析，验证了模型的有效性和优越性。

Prediction of grouting power time series based on FIG and GWO-SVM

This paper proposed a grouting power time series prediction model, based on fuzzy information granulation(FIG)and grey wolf optimized support vector machine(GWO-SVM). The model establishment consisted of three major steps. First, the granular computing method was introduced to decompose the original numerical time series into a series of information granules for reducing the scale of the data. Second, based on the theory of fuzzy sets, the fuzzy set operator was used to carry out the fuzzy calculation for each information granular, so that the obtained fuzzy information granular can reasonably represent the original numerical point set. Finally, the support vector machine was used as a predictive tool, optimized by the grey wolf optimizer algorithm, and the information granules can be predicted quickly, accurately and robustly. Taking an actual project as an example, this model was used to predict the fluctuation range and trend of grouting power time series during the grouting process. After the performance evaluation and comparative analysis, the feasibility and effectiveness of the prediction model were verified.