摘要
针对污水生化处理过程复杂、重要出水指标预测困难且误差比较大的情况,提出了一种基于相关向量机的污水处理出水水质预测模型.首先利用模糊单调递增依赖算法对输入数据进行属性约简,并结合经验确定输入属性,然后利用相关向量机建立预测模型,对模型参数进行寻优,以实现最优预测.实验结果表明,文中提出的预测模型预测精度高、泛化能力强,能较好地满足污水处理出水水质的预测要求.
Considering the complicated process of biochemical sewage treatment, difficulty in precisely forecasting effluent quality and relatively serious prediction error, a prediction model for the effluent quality in wastewater treat- ment is proposed on the basis of relevance vector machine. In this method, an attribute reduction is, first and fore- most, performed for input data by using fuzzy monotonic increasing dependence algorithm, and the final input attributes are determined in combination with experience. Then, an effluent quality prediction model is established with the help of relevance vector machine and the model parameters are subsequently optimized. Experimental results indicate that the proposed prediction model well meets the requirements of effluent quality forecasting due to its high prediction accuracy and strong generalization ability.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第5期103-108,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省科技计划项目(2012A010800027)
广州市珠江科技新星项目(2011J2200084)
华南理工大学中央高校基本科研业务费专项资金重点资助项目(2014ZZ0037)
关键词
污水处理
相关向量机
模糊单调递增
预测
wastewater treatment
relevance vector machine
fuzzy monotonic increasing
forecasting
