In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …

Chaotic systems are the most essential tools for wide range of applications such as
communication, watermarking, data compression and multimedia encryption. However, the …

After a very fast and efficient discriminative broad learning system (BLS) that takes
advantage of flatted structure and incremental learning has been developed, here, a …

Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …

Fault diagnosis, which identifies the root cause of the observed out-of-control status, is
essential to counteracting or eliminating faults in industrial processes. Many conventional …

The broad learning system (BLS) has been proved to be effective and efficient lately. In this
article, several deep variants of BLS are reviewed, and a new adaptive incremental …

This paper proposes a three-stage online deep learning model for time series based on the
ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple …

Approximation ability is of much importance for neural networks. The broad learning system
(BLS)(Chen and Liu, 2018), widely used in the industry with good performance, has been …

Bearings are a core component of rotating machinery, and directly affect its reliability and
operational efficiency. Effective evaluation of a bearing's operational state is key to ensuring …

Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing
models that are inspired by the mechanism of spiking neurons and are 3rd-generation …