CAJ | 학술논문

提出了一种基于遗传算法(GA)优化的最小二乘支持向量机(LSSVM)的MBR膜通量预测算法。为了准确的选择LSSVM的参数，该算法采用GA对LSSVM模型的惩罚因子和核函数参数进行优化。针对MBR膜污染因子较为复杂且各因子之间相互交叉，首先对影响MBR膜通量的各因子进行主成分分析(PCA)，提炼出重要因子作为LSSVM的输入层，膜通量作为输出层，然后建立GA-LSSVM仿真预测模型，并用该预测模型运算得出预测结果。通过对比预测结果和实验数据，得出该算法对膜通量有较高的预测精度，并将其与BP神经网络模型进行了比较，结果表明该预测模型具有更高的预测精度。
제출료일충기우유전산법(GA)우화적최소이승지지향량궤(LSSVM)적MBR막통량예측산법。위료준학적선택LSSVM적삼수，해산법채용GA대LSSVM모형적징벌인자화핵함수삼수진행우화。침대MBR막오염인자교위복잡차각인자지간상호교차，수선대영향MBR막통량적각인자진행주성분분석(PCA)，제련출중요인자작위LSSVM적수입층，막통량작위수출층，연후건립GA-LSSVM방진예측모형，병용해예측모형운산득출예측결과。통과대비예측결과화실험수거，득출해산법대막통량유교고적예측정도，병장기여BP신경망락모형진행료비교，결과표명해예측모형구유경고적예측정도。
This paper proposes a prediction algorithm of MBR membrane flux based on GA and LSSVM. The algo-rithm optimizes the penalty factor and kernel parameters of LSSVM model by genetic algorithm. Because of the diver-sity of the factors that affect MBR membrane fouling, we apply principal component analysis (PCA) to extracting the pivotal factors, then take these factors as the LSSVM input, MBR membrane flux as output, and construct GA-LSSVM model in the end. We get predictive results through the model in the end. By comparing the predicted value with expe-rimental value, the model can forecast MBR membrane flux well. We also use BP neural network model to forecast Membrane flux, and get that the algorithm of GA-LSSVM has higher prediction accuracy.