环境工程学报2011，Vol.5Issue(8)：1723-1728,6.
基于改进混合遗传-支持向量机的CMF产水预测研究
Study on output prediction of CMF based on improved hybrid genetic algorithm and support vector machine
摘要
Abstract
Combined the accelerating genetic algorithm（AGA） and simulated annealing algorithm（SA）,and through improved select tactics and genetic operators to form a new accelerating genetic and simulated annealing algorithm（AGSA）.Based on improved hybrid genetic algorithm and support vector machine（SVM）,it formed a new self-adapting optimized algorithm applied in the SVM parameters.A modified method to develop the flow rate prediction model of continuous micro-filtration（CMF）system was proposed.The prediction models were verified by flow rate experiments in pilot-scale continuous micro-filtration system,results showed that this model could reveal the rule of flow rate variation in CMF.It had small error and strong correlation（R2=0.91,MAE=0.0132,SSE=0.0055,RMSE=0.0155）between predicted values and measured values which explained the model had a strong predictability.Based on leave-one-out cross validation of training samples,the model also showed good robustness（R2=0.89、MAE=0.0164、SSE=0.0073、RMSE=0.0178）.Moreover,the model developed by AGSA-SVM was compared with the model constructed by BP neural network.The former algorithm showed the optimal predictive capability and robustness in the comparison,indicating more suitable for the flow rate prediction of CMF.关键词
连续微滤/支持向量机/加速遗传模拟退火算法/BP神经网络/膜通量Key words
continuous micro-filtration/support vector machine/accelerating genetic and simulated annealing algorithm/BP neural network/membrane flux分类
资源环境引用本文复制引用
许丹宇,王琦,唐运平,张志扬,石岩,孙凯,柴树满..基于改进混合遗传-支持向量机的CMF产水预测研究[J].环境工程学报,2011,5(8):1723-1728,6.基金项目
国家水体污染控制与治理科技重大专项 （）
天津市科技创新专项资金项目 （）
