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

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