Stochastic Configuration Machines for Industrial Artificial Intelligence
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 stochasti...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
DOI: | 10.48550/arxiv.2308.13570 |