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：工业人工智能对具有期望精度的实时预测建模有高度需求，其中神经网络发挥着关键作用。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。本文基于随机配置网络（SCNs），提出一种新的随机化学习器模型，称为随机配置机器（SCMs），旨在强调对工业应用具有实用价值的有效建模与数据规模压缩。与二值化实现的SCNs和随机向量功能连接（RVFL）网络相比，SCMs的模型存储可在保持良好预测性能的同时显著压缩。除SCM学习器模型架构及其学习算法外，作为本工作的重要组成部分，我们还通过分析模型复杂度为SCM的学习能力提供了理论基础。基于多个基准数据集和三项工业应用的实验研究表明，SCM在处理工业数据分析方面具有巨大潜力。