运用神经网络和加速遗传算法建立非线性组合预测模型，在BP算法训练网络出现收敛速度缓慢时启用加速遗传算法（AGA）来优化网络参数，把AGA的优化结果作为BP算法的初始值，再用BP算法训练网络，如此交替运行BP算法和AGA以加快网络的收敛速度，同时改善局部最小问题。最后给出实例研究，结果表明，该方法能明显提高预测精度。

A nonlinear combination forecasting model was established by using neural network and accelerating genetic algorithm (AGA) in the paper. AGA was used to optimize the network parameters as BP approach was slow with training network. Optimization results of AGA were taken as original values of BP approach, the network was trained with BP approach. Network convergence rate was increased with running BP approach and AGA alternately. Meanwhile the part least problem was improved. Examples were presented finally, as a result, the forecasting precision high in evidence.