基于PSO的GM-BP神经网络在基坑预测中的研究

基坑监测对确保基坑工程的顺利实施和周边建筑物的安全尤为重要。由于传统的GM-BP神经网络基坑预测算法在预测过程中连接权值和阈值容易在局部产生最小值,导致无法准确预测基坑周边地面的沉降量。因此,该文提出基于PSO算法的全局搜索优化性,对传统的GM-BP神经网络预测算法中的连接权值和阈值进行不断迭代和优化,并结合基坑的实际监测时间序列验证改进前后预测算法的精度。研究结果表明:改进的基坑预测算法在离基坑5、10、15m三处的RMSE、MAE、MAPE都小于传统的基坑预测算法结果。该方法提高了预测精度,为基坑预测分析提供了技术参考。

基于灰色神经网络的科技论文发表数预测研究

[目的/意义]对我国科技论文发表数量进行较为准确的预测,旨在为有关部门制定相关政策提供参考。[方法/过程]选取我国2005—2019年的相关数据进行实证分析,运用GM-BP神经网络组合预测模型,将灰色系统理论中的灰色关联分析、GM(1,N)模型与BP神经网络相结合,提高了预测的准确度。首先,运用灰色关联分析法对相关因素进行评估,选取灰关联度较高的因素构建GM(1,N)模型;其次,运用GM(1,N)模型的预测结果连同相关因素数据训练BP神经网络;最后,用训练好的BP神经网络进行二次预测并输出为最终预测结果。[结果/结论]与传统GM(1,N)模型相比,所构建的GM-BP神经网络对于科技论文发表量的预测准确度更高。

Prediction of Injection-Production Ratio with BP Neural Network

Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.