CAJ | 학술논문

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。
지지향량궤회귀（SVR）모형재비선성예측방면구유우량성능，기우해모형대공수계통여록변화과정진행예측，병채용이계진탕입자군우화산법（SOPSO）대 SVR 모형삼수진행우화조정，이제고소양본상태하모형적모의정도，증강모형적범화성능。장우화후적 SVR 모형응용우모공수계통여록예측，결과표명：재유한양본상태하，우화후적 SVR 모형적예측평균오차소，명현우우 BP 신경망락모형화 ARX 모형，병구유교강적은건성。해예측모형능교호지해결전통모형재소양본상태하여록예측정도불고、예측효과교차적문제，위연구공수계통여록변화과정급동태예측제공료신적도경。
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.