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This paper presents a modeling method for dynamic process of aluminum electrolysis based on a new neural network.The proposed Neural Network(NN)is based on the theories of Unscented Kalman Filter(UKF)and Strong Tracking Filter(STF),which is shortened as ST-UKFNN in this study.Moreover,the new training algorithm and robustness analysis of the ST-UKFNN are presented.The final section of the paper shows an illustrative example regarding the application of the proposed method to estimate the technical energy consumption of the aluminum electrolysis process,compared with the modeling methods of Back-Propagation Neural Network(BPNN),Extended Kalman Filter Neural Network(EKFNN)and Unscented Kalman Filter Neural Network(UKFNN).The analysis and results show that the method improves the real time tracking ability of dynamic interference in aluminum electrolysis process,and the accuracy of ST-UKFNN is better than the other three modeling methods.