Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.

翻译：在工业人工智能（IAI）中，高精度实时预测建模至关重要，而神经网络在此过程中扮演核心角色。工业人工智能中的神经网络需要强大的高性能计算设备来处理大量浮点数据。基于随机配置网络（SCNs），本文提出一种新的随机学习器模型——随机配置机器（SCMs），以突出对工业应用具有实用价值的有效建模和数据规模缩减。与采用二值化实现的SCNs和随机向量函数连接网络（RVFL nets）相比，SCMs的模型存储可显著压缩，同时保持优越的预测性能。除SCM学习器模型及其学习算法的架构外，作为本文的重要贡献之一，我们还通过分析模型复杂度为SCMs的学习能力提供了理论基础。实验研究在多个基准数据集和三项工业应用上开展，结果表明SCM在处理工业数据分析方面具有巨大潜力。