基于改进的SSA优化BP神经网络的导水断裂带高度预测

王耀国; 李勇永; 郭 涛

基于改进的SSA优化BP神经网络的导水断裂带高度预测

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出版历程
- 发布日期: 2023-02-19

Prediction of height of water flowing fractured zone based on improved SSA to optimize BP neural network

摘要

摘要: 针对在采煤沉降灾变预警建模中导水断裂带高度难以准确预测的问题，提出一种基于改进麻雀搜索算法（SSA）优化BP神经网络的导水断裂带高度预测模型；为增强预测模型的全局搜索和逃离局部最优的能力，在标准SSA中加入Tent混沌映射初始化种群，提高种群分布的均匀性和多样性，并引入高斯变异、高斯扰动算法以及动态步长因子，提高SSA跳出局部最优的能力和求解精度；通过实践应用，将改进的SSA-BP与SSA-BP、PSO-BP、BP以及前人研究的GA-SVR模型预测结果进行对比。结果表明：基于改进的SSA-BP预测模型的平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方根误差（RMSE）和决定系数（R2）分别为1.23 m、2.64%、1.51 m和0.985，均优于其它模型，提高了导水断裂带高度预测的准确性和稳定性。
- 导水断裂带 /
- BP神经网络 /
- 麻雀搜索算法 /
- Tent混沌映射 /
- 高斯变异
Abstract: Aiming at the problem that it is difficult to accurately predict the height of water flowing fractured zone in coal mining subsidence disaster early warning modeling, a prediction model based on improved sparrow search algorithm （ＳＳＡ） to optimize BP neural network is proposed to predict the height of water flowing fractured zone. Add Tent chaotic mapping to the standard SSA to initialize the population, improve the uniformity and diversity of population distribution, and enhance the global search ability of SSA; with the help of Gaussian mutation and Gaussian perturbation algorithm, the ability of SSA to escape from local optimization is improved; dynamic step size is introduced to instead of random step size to improve the solution accuracy of SSA. Through practical application, the improved SSA-BP was compared with SSA-BP, PSO-BP, BP and the prediction results of GA-SVR model studied by predecessors. The results show that the average absolute error （ＭＡＥ）, average absolute percentage error （ＭＡＰＥ）, root mean square error （ＲＭＳＥ） and determination coefficient（Ｒ2） of the improved SSA-BP prediction model are 1.23 m, 2.64%, 1.51 m and 0.985 respectively. It is better than other models and improves the accuracy of predicting the height of water flowing fractured zone.
- water flowing fractured zone /
- BP neural network /
- sparrow search algorithm（SSA） /
- Tent chaotic mapping /
- Gaussian mutation

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