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Real-time predictive modelling with desired accuracy is highly expected inindustrial artificial intelligence (IAI), where neural networks play a keyrole. Neural networks in IAI require powerful, high-performance computingdevices to operate a large number of floating point data. Based on stochasticconfiguration networks (SCNs), this paper proposes a new randomized learnermodel, termed stochastic configuration machines (SCMs), to stress effectivemodelling and data size saving that are useful and valuable for industrialapplications. Compared to SCNs and random vector functional-link (RVFL) netswith binarized implementation, the model storage of SCMs can be significantlycompressed while retaining favourable prediction performance. Besides thearchitecture of the SCM learner model and its learning algorithm, as animportant part of this contribution, we also provide a theoretical basis on thelearning capacity of SCMs by analysing the model's complexity. Experimentalstudies are carried out over some benchmark datasets and three industrialapplications. The results demonstrate that SCM has great potential for dealingwith industrial data analytics.

Stochastic Configuration Machines for Industrial Artificial Intelligence