基于IFWA-BP神经网络的线风速数据融合研究

Research on line wind speed data fusion based on IFWA-BP neural network

针对时差法测量线风速受环境因素影响,导致测量结果不准确的问题,提出一种基于自适应烟花-BP神经网络(IFWA-BP)的数据融合方法。将线风速信息和环境信息进行数据融合,通过多源信息互补减小线风速测量的不准确性。自适应烟花算法是在烟花算法中引入自适应惯性权重,并对爆炸算子进行改进,增强了烟花算法的全局搜索能力,从而优化BP神经网络中的权值和阈值的寻优过程。为了比较IFWA-BP融合模型的融合效果,进行了多算法融合模型对比实验,实验结果表明IFWA-BP融合模型减小了线风速测量的误差,使线风速测量系统的精度达到了98.48%。

Aiming at the problem of inaccurate measurement results caused by the influence of environmental factors on the line wind speed measured by the time difference method, a data fusion method based on adaptive fireworks-BP neural network(IFWA-BP) is proposed. Data fusion of linear wind speed information and environmental information is used to reduce the inaccuracy of linear wind speed measurement through multi-source information complementation. The adaptive firework algorithm introduces adaptive inertia weights into the firework algorithm and improves the explosion operator to enhance the global search ability of the firework algorithm, thereby optimizing the optimization process of the weights and thresholds in the BP neural network. In order to compare the fusion effect of the IFWA-BP fusion model, a multi-algorithm fusion model comparison experiment was carried out. The experimental results show that the IFWA-BP fusion model reduces the error of linear wind speed measurement and makes the accuracy of the linear wind speed measurement system reach 98.48%.