改进麻雀搜索算法的RBF神经网络水质预测  

RBF Neural Network Water Quality Prediction Based on Improved Sparrow Search Algorithm

作　　者：宋健 丛秋梅[1] 杨帅帅 杨健 SONG Jian;CONG Qiu-Mei;YANG Shuai-Shuai;YANG Jian(School of Information and Control Engineering,Liaoning Petrochemical University,Fushun 113001,China)

机构地区：[1]辽宁石油化工大学信息与控制工程学院,抚顺113001

出　　处：《计算机系统应用》2023年第4期255-261,共7页Computer Systems & Applications

基　　金：国家自然科学基金(61803191);辽宁省自然科学基金(2019-KF-03-05)。

摘　　要：针对污水处理过程中化学需氧量(chemical oxygen demand,COD)难以在线测量的问题,提出了一种基于径向基函数(radial basis function,RBF)神经网络的软测量模型.首先,用污水处理厂实测数据挑选出与COD相关的过程变量作为输入变量;其次,基于RBF神经网络建立出水COD软测量模型,利用自适应遗传算法改进的麻雀搜索算法(adaptive genetic algorithm improved sparrow search algorithm,AGAISSA)优化RBF神经网络的中心值、宽度值以及权值,通过改进麻雀位置更新公式以及引入遗传算法中的自适应交叉和变异操作保证了软测量模型的精度;最后,将RBF神经网络的软测量模型应用于污水处理厂实测数据加以验证,结果表明:AGAISSA优化RBF神经网络模型能够对出水COD进行准确的预测,具有较高的预测精度.To address the problem that chemical oxygen demand(COD)is difficult to be measured on line during sewage treatment,this study proposes a soft sensing model based on a radial basis function(RBF)neural network.First,the process variables related to COD are selected as input variables by using the measured data of a sewage treatment plant.Second,the soft sensing model of COD in effluent is built on the basis of an RBF neural network.The center value,width value,and weight of the RBF neural network are optimized by an adaptive genetic algorithm improved sparrow search algorithm(AGAISSA).The accuracy of the soft sensing model is ensured by improving the sparrow position update formula and introducing the adaptive crossover and mutation operation in the genetic algorithm.Finally,the soft sensing model based on the RBF neural network is applied to the measured data of a sewage treatment plant for verification.The results show that the AGAISSA optimized RBF neural network model can accurately predict the COD in effluent and has high prediction accuracy.

关 键 词：污水处理 麻雀搜索算法 自适应遗传算法 RBF神经网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] X703[自动化与计算机技术—控制科学与工程]