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 im-proved algorithm is taken to train two networks respectively and the output values are obtainod;Secoudiy,the linear mapping net-work is optimized by self-adaptive genetic algorithm to gain higher prediction accuracy;Finally,this paper has carded out two ex-periments 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 com-mon tool for complex prediction.%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.
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