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：面向工业人工智能（IAI）中高精度实时预测建模的需求，神经网络发挥着关键作用。IAI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）提出一种新型随机学习器模型——随机配置机器（SCMs），旨在实现工业应用中具有实用价值的有效建模与数据规模缩减。相较于SCNs及采用二值化实现的随机向量函数连接网络（RVFL net），SCMs可在保持优异预测性能的同时显著压缩模型存储空间。除SCM学习器模型架构及其学习算法外，作为本项研究的重要贡献，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。在多个基准数据集及三项工业应用上的实验研究表明，SCM在工业数据分析领域具有巨大潜力。