基于PSO-SVR模型的供水系统余氯预测研究

支持向量机回归(SVR)模型在非线性预测方面具有优良性能,基于该模型对供水系统余氯变化过程进行预测,并采用二阶振荡粒子群优化算法(SOPSO)对SVR模型参数进行优化调整,以提高小样本状态下模型的模拟精度,增强模型的泛化性能。将优化后的SVR模型应用于某供水系统余氯预测,结果表明:在有限样本状态下,优化后的SVR模型的预测平均误差小,明显优于BP神经网络模型和ARX模型,并具有较强的稳健性。该预测模型能较好地解决传统模型在小样本状态下余氯预测精度不高、预测效果较差的问题,为研究供水系统余氯变化过程及动态预测提供了新的途径。

Residual Chlorine Prediction in Water Supply System Based on Support Vector Machine Regression Model Optimized by PSO Method

In view of its excellent prediction performance of support vector machine regression (SVR)model for nonlinear system,a model of residual chlorine prediction was put forward to predict changes in water supply system based on SVR.Moreover,two-order oscillating particle swarm optimization algorithm (SOPSO)was employed to optimize the SVR model parameters in order to enhance the model precision in small sample situations and improve the generalization ability of the model.This optimized model was applied to predict the residual chlorine in a water supply system,and the results showed that:in the case of limited samples,the average prediction error of the opti-mized SVR model is 3.86%,which is better than that of BP and ARX prediction models,and also has strong stabil-ity.This model could solve the problems of low fitting accuracy and poor efficacy of prediction which often appear by traditional models.It provides a new approach for the model construction and algorithm selection in residual chlo-rine prediction for water supply system.