基于COBP模型的城市短期需水量预测研究  

Study on Short-Term Water Demand Forecast of City Based on COBP Model

作　　者：叶强强 王景成 陈超波[1] 王召[1] 涂吉昌 YE Qiangqiang;WANG Jingcheng;CHEN Chaobo;WANG Zhao;TU Jichang(Xi'an Technological University,Xi'an 710021;Department of Automation,Shanghai Jiao Tong University,Shanghai 200240)

机构地区：[1]西安工业大学,西安710021 [2]上海交通大学自动化系,上海200240

出　　处：《计算机与数字工程》2020年第1期198-205,共8页Computer & Digital Engineering

基　　金：陕西省工业领域重点研发计划项目（编号：2018ZDXM-GY-168）资助

摘　　要：针对城市需水量预测中时间序列的非线性特性及传统BP网络预测收敛速度慢易陷入局部极小值等问题,将Chaos理论和BP神经网络理论相结合,提出了一种基于Chaos-BP理论的城市短期需水量COBP(ChaosBackPropagtion)预测模型。利用重构相空间的嵌入维数确定COBP网络的结构,通过混沌优化搜索,找到BP神经网络权值的全局最优值,并对其输出的“尖点”预测值进行混沌参数控制,实现城市短期需水量的预测。仿真分析表明,与传统预测模型相比,COBP预测模型所需训练数据样本少,收敛速度快、易达到全局最小值,预测结果整体误差的指标良好,呈现良好的综合预测性能。In view of the nonlinear characteristics of time series in urban water demand forecasting and the problem of slow convergence rate of traditional BP neural network and local minimum,the Chaos theory and BP neural network theory are combined.This paper presents a forecasting model of short term urban water demand COBP(Chaos Back Propagation)based on Chaos-BP theo ry.The structure of COBP network is determined by embedding dimension of reconstructed phase space,the global optimal value of BP neural network weight is found by chaos optimization search,and chaotic parameter control is carried out on the prediction value of"cusp"of BP neural network,so that the forecast of urban short-term water demand can be realized.The simulation results show that compared with the traditional prediction model,the COBP prediction model requires fewer training data samples,faster conver gence speed,easier to reach the global minimum value,and the overall error index of the prediction results is good,showing a good comprehensive prediction performance.

关 键 词：城市需水量预测 COBP模型 重构相空间 混沌优化搜索 混沌参数控制 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]