Dynamic evolutionary model based on a multi-sampling inherited HAPFNN for an aluminium electrolysis manufacturing system

Abstract

It is technically difficult to accurately establish a dynamic adaptive model of an aluminium electrolysis manufacturing system (AEMS) because the system has many complex characteristics, such as multiple parameters, dynamic time variance, and a non-Gaussian distribution of process data. Inspired by the overall superiority of particle filtering theory in dealing with non-linear non-Gaussian problems, this paper presents a novel method based on a multi-sampling inherited hybrid annealed particle filter neural network (MSI-HAPFNN). Firstly, the neural network’s (NN’s) weights and thresholds are used as the state variables of hybrid annealed particle filter; Secondly, the hybrid proposal distribution obtained by sampling the above state variables is employed to replace the posterior proposal distribution in the standard particle filter (PF) algorithm as the importance density function, thereby adjusting the NN’s weights and thresholds in real time. Thirdly, the model achieves the features of multi-sampling and inheritance by introducing NN and PF weights, and using adaptive inheritance method. Therefore, this paper systematically proposes the theoretical construction framework and experimental procedure of MSI-HAPFNN. Furthermore, this article also introduces a genetic algorithm to thoroughly evaluate the prediction potential. The proposed model has been tested on the real-world system for aluminium electrolysis manufacturing and compared with several closely related frameworks. The experimental results show that the MSI-HAPFNN model can significantly improve the self-adaptive ability of the object system to working conditions and the prediction accuracy of power consumption, which is helpful in finding optimal design parameters in an AEMS.