Online semisupervised broad learning system for industrial fault diagnosis
X Pu, C Li - IEEE transactions on industrial informatics, 2021 - ieeexplore.ieee.org
Recently, broad learning system (BLS) has been introduced to solve industrial fault
diagnosis problems and has achieved impressive performance. As a flat network, BLS …
diagnosis problems and has achieved impressive performance. As a flat network, BLS …
[HTML][HTML] An online ensemble semi-supervised classification framework for air combat target maneuver recognition
XI Zhifei, LYU Yue, KOU Yingxin, LI Zhanwu… - Chinese Journal of …, 2023 - Elsevier
Online target maneuver recognition is an important prerequisite for air combat situation
recognition and maneuver decision-making. Conventional target maneuver recognition …
recognition and maneuver decision-making. Conventional target maneuver recognition …
A real-time adaptive fault diagnosis scheme for dynamic systems with performance degradation
The degradation of a system's performance poses a significant challenge to the effective
application of fault diagnosis methods for dynamic systems. Consequently, the underlying …
application of fault diagnosis methods for dynamic systems. Consequently, the underlying …
AUC-based extreme learning machines for supervised and semi-supervised imbalanced classification
Extreme learning machines (ELMs) has been theoretically and experimentally proved to
achieve promising performance at a fast learning speed for supervised classification tasks …
achieve promising performance at a fast learning speed for supervised classification tasks …
Class-incremental learning method with fast update and high retainability based on broad learning system
Machine learning aims to generate a predictive model from a training dataset of a fixed
number of known classes. However, many real-world applications (such as health …
number of known classes. However, many real-world applications (such as health …
Evidential ensemble preference-guided learning approach for real-time multimode fault diagnosis
Operational changes in industrial production can alter system operating modes, which
complicates real-time fault diagnosis by affecting sensor data and fault characteristics. In …
complicates real-time fault diagnosis by affecting sensor data and fault characteristics. In …
Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification
In this paper, we suggest a novel approach termed as regularized based implicit Lagrangian
twin extreme learning machine in primal as a pair of unconstrained convex minimization …
twin extreme learning machine in primal as a pair of unconstrained convex minimization …
Regularized correntropy criterion based semi-supervised ELM
Along with the explosive growing of data, semi-supervised learning attracts increasing
attention in the past years due to its powerful capability in labeling unlabeled data and …
attention in the past years due to its powerful capability in labeling unlabeled data and …
Robust adaptive semi-supervised classification method based on dynamic graph and self-paced learning
Despite the computers have developed rapidly in recent years, there are still many
difficulties to obtain a large number of labelled data in many practical problems, for example …
difficulties to obtain a large number of labelled data in many practical problems, for example …
Robust online multilabel learning under dynamic changes in data distribution with labels
In this paper, a robust online multilabel learning method dealing with dynamically changing
multilabel data streams is proposed. The proposed method has three advantages: 1) higher …
multilabel data streams is proposed. The proposed method has three advantages: 1) higher …