基于进化神经网络的灰色预测模型

Grey forecasting model based on evolving neural network

为了弥补神经网络用于灰色理论中学习效率低、收敛速度慢、易陷入局部最优等缺点,采用了改进遗传算法IGA(im proved genetic algon ithm)来辅助优化神经网络,实现了对网络连接权的自适应进化,并应用于灰色预测中。提出了进化神经网络灰色预测模型,通过M atlab程序对某地区农村人均收入的数据进行了预测、比较,预测结果误差均在0.3%以下,明显优于传统的GM(1,1)(grey m odel)的预测结果,其误差在10%左右。实例验证,将进化神经网络应用于灰色预测模型中是可行和有效的。

Grey model based on the ANN （artifical neural network） is better than traditional GM （1,1） （grey model）, which has been verified. However, the low learning efficiency, slow procedure and local minimum which the procedure is trapped in are the defects of ANN. The network＇s optimization was aided by improved GA, network＇s connection weights were self-adaptively evolved and the network was applied in grey forecast model. A hybrid forecast model was proposed. This grey forecast model based on evolving neural network, combines GA and ANN and GM（1,1） together. The simulation program based on Matlab shows that this hybrid algorithm is obviously feasible and effective.