基于灰色遗传BP神经网络的校园区间需水预测研究

水资源预测是城市安全用水的基础保障,而校园用水预测是城市用水规划和管理的组成部分。针对校园用水受很多因素影响产生的不确定性,提出了基于灰色遗传BP神经的校园用水预测模型。模型对校园用水的数据进行灰色关联分析,并加入遗传算法去优化BP神经网络,经过残差计算,输出区间的预测值。运用该模型可以充分提取小样本信息,解决神经网络无法自动寻优的问题。通过Matlab对校园的用水区间数据进行仿真,得出的结果显示,预测的数据和实际数据基本吻合,其仿真精度可以达到90. 32%,验证了该方法的可行性,此预测方法有一定的借鉴意义。

On campus interval water demand prediction based on grey genetic BP neural network

Water resource prediction is the basic guarantee of urban safe water use, while campus water use prediction is the basis of urban water use planning and management. Aiming at solving the uncertainty of campus water consumption caused by several factors, a prediction model of campus water consumption based on grey genetic BP neural network is proposed. The model carries out grey relational analysis on the data of campus water consumption, and adds genetic algorithm to optimize BP neural network. After residual calculation, the predicted value of the output interval is obtained. This model can fully extract information from small samples and add genetic optimization neural network to solve the problem that neural network cannot automatically optimize. The results of Matlab simulation of campus water use interval data show that the predicted data and actual data are basically consistent with a simulation accuracy 90.32%, and it verified the feasibility of the method, the prediction method has some reference significance.