We optimize the back propagation neural network(BPNN) by using particle swarm optimization(PSO), and establish an optimized GPT3 model(MGPT3). We use the zenith tropospheric delay (ZTD) data of 30 IGS stations in Europe for 366 days in 2020 as an example to compare the accurac...
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Secondly, based on the back-propagation neural network(BPNN) algorithm, the gradient optimization process of the BPNN algorithm is improved by using the single exponential smoothing(SES) method. In the third step, particle swarm optimization(PSO) is used to optimize the main hyper-paramete...
该方法首先利用小波分解技术将风机净空值的原始数据分解为不同频率的子序列,以捕捉数据的局部特征和趋势,然后引入PSO算法对BPNN的权值和阈值进行优化,以改善神经网络的预测性能,提高风机叶片净空值的预测精度和稳定性.实验结果表明,基于Sym-PSO-BPNN模型的风机叶片净空值预测方法具有较高的预测精度和可靠性,能够为风电场...