基于AIGA—BP神经网络的粮食产量预测研究

Research on forecast of grain production based on AIGA—BP neural network

针对BP神经网络进行粮食产量预测时易陷入局部最优的缺陷,本研究借鉴免疫算法和遗传算法的优点,设计具有浓度调解机制和全局寻优特性的自适应免疫遗传算法(AIGA),用该算法来优化BP神经网络的权值和阈值,并最终给出AIGA—BP神经网络的MapReduce实现。通过仿真实验验证:在精准度方面,AIGA—BP神经网络对于粮食产量预测的精度要明显优于BP神经网络;在效率方面,该算法的MapReduce实现具有更快的处理速度和良好的加速比,在使用大尺寸训练集时优势更为明显。

Aiming at the problem of trapping in local optimum when BP neural network forecasts grain production,this research refers to advantages of immune algorithm and genetic algorithm,designs the adaptive immune genetic algorithm（AIGA）which has concentration regulatory mechanism and global optimization performance.With this algorithm,weights and thresholds of BP neural network can be optimized,and it finally realizes AIGA—BP neural network implementation in MapReduce.In respect of accuracy,simulating experiments verifies that the accuracy of AIGA—BP neural network,which predicts grain production,outperformed BP neural network.In terms of efficiency,this algorithm processes faster and has a good speed up in MapReduce implementation,and it shows obvious advantages especially in large scale training sets.