基于CSO优化模糊神经网络的污水处理出水COD预测模型  

Effluent COD Prediction Model of Sewage Treatment Based on CSO Optimized Fuzzy Neural Network

作　　者：沈鹏 李明河 张陈 SHEN Peng;LI Minghe;ZHANG Chen(School of Electrical and Information Engineering,Anhui University of Technology,Maanshan 243032,China)

机构地区：[1]安徽工业大学电气与信息工程学院,安徽马鞍山243032

出　　处：《现代信息科技》2022年第23期72-76,共5页Modern Information Technology

基　　金：国家科技支撑计划课题(2014BAC01B04)。

摘　　要：针对污水处理的非线性系统,为了能够有效预测出水化学需氧量(COD)。提出了一种基于鸡群算法(CSO)算法优化的模糊神经网络预测模型。首先通过模糊神经网络设计了COD模糊神经网络预测模型;之后采用鸡群算法优化模糊神经网络模型参数,弥补预测模型容易陷入局部极小值的缺点,使模糊神经网络的预测精度有了明显提高。最后用MATLAB平台进行仿真实验,仿真结果清晰表明,改进型模糊神经网络预测模型具有很好的自适应性和鲁棒性,提高了COD预测精度和预测效果,能够满足实际污水处理的测量需求,具有一定的研究价值。Aiming at the nonlinear system of sewage treatment,in order to effectively predict the Chemical Oxygen Demand(COD)of effluent,this paper presents a fuzzy neural network prediction model based on CSO algorithm optimization.Firstly,the COD fuzzy neural network prediction model is designed by fuzzy neural network.Then,the Competitive Swarm Optimizer(CSO)is used to optimize the parameters of the fuzzy neural network model,which makes up for the shortcoming that the prediction model is easy to fall into local minima,so that the prediction accuracy of the fuzzy neural network has been significantly improved.Finally,the simulation experiment is carried out with MATLAB platform.The simulation results clearly show that the improved fuzzy neural network prediction model has good adaptability and robustness,improves the prediction accuracy and prediction effect of COD,and it can meet the measurement needs of actual sewage treatment,which has a certain research value.

关 键 词：模糊神经网络 预测模型 出水COD 污水处理 CSO算法 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]