| PSO-SVM与BP神经网络组合预测供水系统余氯的方法 |
| |
| 引用本文： | 毛湘云,徐冰峰,孟繁艺. PSO-SVM与BP神经网络组合预测供水系统余氯的方法[J]. 重庆建筑大学学报, 2019, 41(4): 159-164 |
| |
| 作者姓名： | 毛湘云 徐冰峰 孟繁艺 |
| |
| 作者单位： | 昆明理工大学 建筑工程学院, 昆明 650500,昆明理工大学 建筑工程学院, 昆明 650500,昆明理工大学 建筑工程学院, 昆明 650500 |
| |
| 摘 要： | 针对余氯量在供水系统内非线性变化的特性，建立了PSO-SVM与BP神经网络组合模型对管网末端余氯进行预测分析。该模型通过粒子群优化算法（PSO），对SVM的特性参数进行优化；采用BP神经网络对模型进行残差修正。通过对单一的BP模型和SVM模型、组合模型的预测精度进行分析。结果表明：组合模型预测比BP和SVM单一预测均方误差分别降低了62.30%、75.29%，平均相对误差降低了55.03%、54.27%。综上所述，该模型具有强大的非线性拟合能力，预测精度高，运行稳定性强，对供水企业控制余氯的投加量和设置二次加氯点有一定的指导作用。
|
| 关 键 词： | 余氯 支持向量机 粒子群算法 神经网络 组合模型 |
| 收稿时间： | 2018-11-24 |
Prediction of residual chlorine in water supply system by PSO-SVM and BP neural network combined model |
| |
| Mao Xiangyun,Xu Bingfeng and Meng Fanyi. Prediction of residual chlorine in water supply system by PSO-SVM and BP neural network combined model[J]. Journal of Chongqing Jianzhu University, 2019, 41(4): 159-164 |
| |
| Authors: | Mao Xiangyun Xu Bingfeng Meng Fanyi |
| |
| Affiliation: | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, P. R. China,Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, P. R. China and Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, P. R. China |
| |
| Abstract: | Due to the nonlinearity of residual chlorine in the pipe network, a PSO-SVM and BP neural network combined model was developed to prediction of residual chlorine.This model through particle swarm optimization algorithm (PSO) to optimization the characteristics parameter of the SVM, and use the BP neural network model to residual error correction. The prediction precision of combined model was ananysed by comparing the single prediction model of BP and SVM. The results show that compared with the single prediction of BP and SVM, the mean square error of the combined model decreased by 62.30% and 75.29% respectively, but the average relative error decreased by 55.03% and 54.27% respectively. In a conclusion, the combined model had strong nonlinear fitting capability, high prediction accuracy, and strong operation stability. This model plays an important role in controlling the residual chlorine dosing and setting the secondary chlorination point for water supply enterprise. |
| |
| Keywords: | residual chlorine support vector machines particle swarm optimization neural networks combined model |
|
| 点击此处可从《重庆建筑大学学报》浏览原始摘要信息 |
|
点击此处可从《重庆建筑大学学报》下载全文 |
|
