摘要
针对BP神经网络算法通常具有收敛速度慢且容易陷入局部极小值的缺点,在对国内空调订单市场进行分析和研究的基础上,提出一种用遗传算法优化灰色神经网络模型参数的方法,该方法利用灰色模型(Grey Model,GM)弱化数据的随机性以及神经网络的高度非线性,对空调订单建立了一种非线性预测模型,并采用遗传算法对其进行优化,从而提高了预测的精度并加快了收敛程度。仿真结果表明该算法能较好的解决空调订单预测的问题并可推广到同类预测中。
BP algorithm usually has slow convergence speed and is easy to fall into local minimum value. On the basis of the analysis and study for the domestic market of air conditioning order. This article proposes a method of the model parameters of grey neural net- work optimized by genetic algorithm. By utilizing the property of gray model that the randomness of data can be reduced and the strong nonlinearity of neural network, the method establishes a non-linear prediction model for air conditioning demand, and the genetic algo- rithm is used to optimize it so as to improve the accuracy of forecasting and speed up the degree of convergence. Simulation results indi- cate that the algorithm can better solve the problem of air conditioner order forecasting and can be widely apply to similar prediction.
出处
《控制工程》
CSCD
北大核心
2013年第5期934-937,共4页
Control Engineering of China
基金
国家自然科学基金资助项目(51075291)
山西省自然科学基金资助项目(2011011011-1)
关键词
神经网络
遗传算法
灰色模型
neural network
genetic algorithm （GA）
gray model （GM）
