用径向基函数神经网络方法预报太阳黑子数平滑月均值

简单介绍了径向基函数神经网络方法的原理和应用，发展了用径向基函数（RBF）对平滑月平均黑子数进行预报的方法. 用不同的数据序列对网络进行训练，对未来8个月的平滑月平均黑子数进行预报. 用该方法对第23周开始后的平滑月平均黑子数进行逐月预报，并与实测值进行比较，结果表明随着预报实效的延长预报误差被逐渐放大，该方法可以较准确地做出未来４个月的预报，绝对误差可以控制在20以内，标准差为4.8，相对误差控制在38％以内，大部分相对误差不超过15%（占总预报数的89％），具有较好的应用价值. 用于网络训练的样本数量对预报结果会产生一定的影响.

Prediction of the smoothed monthly mean sunspot numbers by means of radial basis function neural networks

The Radial Basis Function (RBF) neural networks method is introduced and applied to the smoothed monthly mean sunspot number's (SMMSN) prediction for cycle 23 in this paper. Prediction methods are made respectively for predicting of SMMSNs for the next eight months by training the neural networks with different sets of data. A comparison of the SMMSN's predictions one to eight months in advance with the derived ones from the observational data for absolutely the most part of cycle 23 shows that this RBF neural networks method should be an applicable one for the mid-term solar activity forecast. A brief discussion give in the last section of this paper points out: ① that the error of the prediction increases along with the time in advance, while for the prediction with an advanced time of ≤4 months the error can be controlled under 4.8 and 38%, and for 89% of this kind of prediction the relative error is　≤15%. ② that size of the data set used for the training of the RBF neural networks would give an effect to the predicting ability of the prediction model.