计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 138-142.
龙云,贺新光,章新平
LONG Yun, HE Xin-guang and ZHANG Xin-ping
摘要: 为了提高月降水量预测精度和处理神经网络隐藏层神经元个数优化问题,提出了一种基于粒子群优化的小波多神经网络模型,并将其应用于洞庭湖流域月降水量的预测。首先,将大尺度气候指数和标准化月降水量作为预测因子在不同时间尺度上分解,然后使用多个基于粒子群算法以确定各隐藏层神经元个数的cascade-forward(CF)神经网络,用以对各频率下的标准月降水量子序列分别进行预测,最后通过重构和逆标准化得到月降水量预测值。结果表明:基于粒子群优化的小波多神经网络的预测精度高于小波单神经网络的预测精度,并且对极端月降水量的预测也有所改善。
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