Artificial neural networks have a broad array of applications today due to their high degree of flexibility and ability to model nonlinear functions from data. However, the trustworthiness of neural networks is limited due to their black-box nature, their poor ability to generalize from small datasets, and their inconsistent convergence during training. Aluminum electrolysis is a complex nonlinear process with many interrelated sub-processes. Artificial neural networks can potentially be well suited for modeling the aluminum electrolysis process, but the safety-critical nature of this process requires trustworthy models. In this work, sparse neural networks are trained to model the system dynamics of an aluminum electrolysis simulator. The sparse model structure has a significantly reduction in model complexity compared to a corresponding dense neural network. We argue that this makes the model more interpretable. Furthermore, the empirical study shows that the sparse models generalize better from small training sets than dense neural networks. Moreover, training an ensemble of sparse neural networks with different parameter initializations show that the models converge to similar model structures with similar learned input features.
翻译:人工神经网络由于具有高度的灵活性和从数据中模拟非线性功能的能力,因此今天应用范围很广。然而,神经网络的可信任性有限,原因是其黑箱性质、从小数据集中概括的能力有限、培训过程中的趋同不一致。铝电解是一个复杂的非线性过程,有许多相互关联的次工艺。人工神经网络可能非常适合模拟铝电解过程,但这一过程的安全临界性质需要可靠的模型。在这一工作中,稀有的神经网络经过培训,可以模拟一个光化电解模拟器的系统动态。稀有的模型结构与一个相应的密集神经网络相比,其模型复杂性大大降低。我们说,这使得模型更容易被解释。此外,实证研究表明,稀有的模型比密集的神经网络从小的训练机群中概括得更好。此外,培训具有不同参数初始化的稀有神经网络的组合表明,模型与类似的模型结构相近似,具有类似的学习输入特征。