基于自适应双层无迹卡尔曼滤波神经网络的铝电解电流效率预测模型  

Prediction model for the current efficiency of aluminum electrolysis based on the adaptive double layer unscented Kalman filter neural network

作　　者：方小燕 姚立忠 罗海军[2,3] 张玉泽 易军 FANG Xiao-yan;YAO Li-zhong;LUO Hai-jun;ZHANG Yu-ze;YI Jun(School of Electronic and Electrical Engineering,Chongqing University of Science and Technology,Chongqing 401331,China;College of Physics and Electronic Engineering,Chongqing Normal University,Chongqing 401331,China;National Center for Applied Mathematics in Chongqing,Chongqing 401331,China;School of Computer Science and Engineering,Chongqing University of Science and Technology,Chongqing 401331,China)

机构地区：[1]重庆科技大学电子与电气工程学院,重庆401331 [2]重庆师范大学物理与电子工程学院,重庆401331 [3]重庆国家应用数学中心,重庆401331 [4]重庆科技大学计算机科学与工程学院,重庆401331

出　　处：《控制理论与应用》2025年第3期579-589,共11页Control Theory & Applications

摘　　要：针对铝电解过程强干扰和强时变导致模型精确度和稳定性不佳的难题,本文提出一种基于自适应双层无迹卡尔曼滤波神经网络的建模方法.该方法首先构建一种双层无迹卡尔曼滤波神经网络模型,以提高模型对扰动系统的稳定性.具体为:使用双层无迹卡尔曼滤波在线更新神经网络的权值和阈值;然后,在双层无迹卡尔曼滤波神经网络的状态变量均方误差中引入约束调节参数;同时,采用梯度下降法自适应调整比例调节参数,将其均方误差约束至较小的范围内,以此来削弱滤波递归计算过程中误差累积对模型的影响;最后,通过铝电解电流效率预测,验证了本文所提方法具有较高的精确度和稳定性.This paper presents a novel modeling method based on an adaptive double layer unscented Kalman filter neural network(ADLUKFNN),which tackles the challenges of poor model accuracy and stability resulting from strong interference and time-varying disturbances in the aluminum electrolysis process.Firstly,this method constructs a double layer unscented Kalman filter neural network(DLUKFNN)model to enhance the stability of the model towards the disturbance system.Specifically,the weights and thresholds of the neural network are updated online using the double layer unscented Kalman filter.Then,a constraint adjustment parameter is introduced into the mean square error of the state variables in DLUKFNN.Meanwhile,by employing the gradient descent method to adaptively adjust the constraint adjustment parameter,the mean square error is constrained within a smaller range,thereby weakening the impact of error accumulation during the filtering recursive calculation on the model.Finally,the accuracy and stability of the proposed method are verified through aluminum electrolysis current efficiency prediction.

关 键 词：铝电解 自适应建模 双层无迹卡尔曼滤波 人工神经网络 电流效率 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]