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.

准确地建立铝电解制造系统（AEMS）的动态自适应模型在技术上是困难的，因为该系统具有许多复杂的特性，例如多个参数，动态时间变化和过程数据的非高斯分布。受粒子滤波理论在处理非线性非高斯问题方面的整体优势的启发，本文提出了一种基于多采样遗传混合退火粒子滤波神经网络（MSI-HAPFNN）的新方法。首先，将神经网络的权重和阈值用作混合退火粒子滤波器的状态变量。其次，通过对上述状态变量进行采样得到的混合提议分布被用来代替标准粒子滤波（PF）算法中的后提议分布作为重要性密度函数，从而实时调整NN的权重和阈值。第三，该模型通过引入NN和PF权重，并采用自适应继承方法，实现了多重采样和继承的特征。因此，本文系统地提出了MSI-HAPFNN的理论构建框架和实验程序。此外，本文还介绍了一种遗传算法来全面评估预测潜力。所提议的模型已经在铝电解制造的实际系统上进行了测试，并与几个紧密相关的框架进行了比较。