Abstract: Single neural network is difficult in performing accurate predictions for complex model.A hybrid model，which involves a radial basis function network，a multi-layer perception network with back-propagation and a control module，is proposed and used for forecasting complex system.The control module serves as a linear mapping network which combines the outputs of two neural networks to gain the final output value.The prediction methods of the hybrid model are mainly discussed：Firstly，the improved algorithm is taken to train two networks respectively and the output values are obtained；Secondly，the linear mapping network is optimized by self-adaptive genetic algorithm to gain higher prediction accuracy；Finally，this paper has carried out two experiments to compare the prediction performance of a single network and the proposed model.The experimental results show that the proposed hybrid neural network provides a superior performance in prediction accuracy than other methods and offers a common tool for complex prediction.

Key words: radial basis function, Back Propagation（BP） neural network, hybrid network model, data prediction, linear mapping

摘要： 单一神经网络难以对复杂模型做出准确的预测，提出了一种并联型混合神经网络模型用于对复杂的系统进行预测，该模型由径向基函数网络、BP网络和控制模块组成。控制模块用于线性映射层，将两种单一神经网络的输出结合并得到最终的输出结果。详细地给出了混合模型的预测方法：首先，利用改进算法分别训练径向基函数网络和BP网络；其次，采用自适应遗传算法优化线性映射层以获得更好的预测精度；最后，利用两个实例比较单一神经网络和提出的混合网络的预测性能。实验表明，混合神经网络在预测精度上比单一网络具有更优的性能，同时，该混合模型为复杂系统提供了一种通用的预测工具。

关键词: 径向基函数, BP神经网络, 混合网络模型, 数据预测, 线性映射