Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery
The distribution of monitored data during the service life of machinery equipment is
imbalanced, especially there is more monitoring data for health conditions than for failure …
imbalanced, especially there is more monitoring data for health conditions than for failure …
Cross-domain privacy-preserving broad network for fault diagnosis of rotating machinery
The broad learning system-based data processing method has been widely used in the field
of industrial intelligent operation and maintenance, and has achieved impressive results …
of industrial intelligent operation and maintenance, and has achieved impressive results …
Eigen-Entropy: A metric for multivariate sampling decisions
Sampling is a technique to help identify a representative data subset that captures the
characteristics of the whole dataset. Most existing sampling algorithms require distribution …
characteristics of the whole dataset. Most existing sampling algorithms require distribution …
A real-time DC faults diagnosis in a DC ring microgrid by using derivative current based optimal weighted broad learning system
This paper presents a novel approach for DC faults diagnosis in renewables based DC-ring
microgrid (DC-RM). The proposed novel approach consists of a second-order derivative …
microgrid (DC-RM). The proposed novel approach consists of a second-order derivative …
Pseudo inverse versus iterated projection: Novel learning approach and its application on broad learning system
Broad learning system (BLS) has attracted widespread attention owing to its concise
structure and efficient incremental learning based on ridge regression approximation of …
structure and efficient incremental learning based on ridge regression approximation of …
A partial domain adaptation broad learning system for machinery fault diagnosis
A Qin, Q Hu, Q Zhang, H Mao - Measurement, 2025 - Elsevier
In order to accurately diagnose faults across different domains where the fault types are
inconsistent between the source and target domains, a cross-domain fault diagnosis model …
inconsistent between the source and target domains, a cross-domain fault diagnosis model …
[HTML][HTML] Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization
This paper presents a new method for designing matched digital filters with discrete valued
coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been …
coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been …
Factorization of broad expansion for broad learning system
The broad learning system (BLS) based on the random vector functional link neural network
is a new versatile non-iterative neural network for rapidly selecting models. One of its …
is a new versatile non-iterative neural network for rapidly selecting models. One of its …
A new robust projection distributed broad learning under redundant samples and noisy environment
H Liu, H Pan, J Zheng, J Tong… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Broad learning system (BLS) is a breadth-based learning algorithm based on single-layer
feedforward network (SLFN), which has the advantages of incremental learning with its fast …
feedforward network (SLFN), which has the advantages of incremental learning with its fast …
Fuzzy style flat-based clustering
S Gu, FL Chung, S Wang - Information Sciences, 2023 - Elsevier
The recently developed fuzzy style k-plane clustering (S-KPC) algorithm displays promising
clustering quality by leveraging both similarities and distinguishable styles between samples …
clustering quality by leveraging both similarities and distinguishable styles between samples …