A Multiple-Kernel Relevance Vector Machine with Nonlinear Decreasing Inertia Weight PSO for State Prediction of Bearing
The scientific and accurate prediction for state of bearing is the key to ensure its safe operation. A multiple-kernel relevance vector machine (MkRVM) including RBF kernel and polynomial kernel is proposed for state prediction of bearing in this study; the proportions of RBF kernel and polynomial k...
Saved in:
Main Authors: | Sheng-wei Fei, Yong He |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2015-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2015/685979 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control
by: Joshua Sunder David Reddipogu, et al.
Published: (2020-01-01) -
An Algorithm for Optimal Allocation of Water Resources in Receiving Areas Based on Adaptive Decreasing Inertia Weights
by: Fei Li, et al.
Published: (2022-01-01) -
The Hybrid Method of VMD-PSR-SVD and Improved Binary PSO-KNN for Fault Diagnosis of Bearing
by: Sheng-wei Fei
Published: (2019-01-01) -
Multiple Weighted Estimates for Vector-Valued Multilinear Singular Integrals with Non-Smooth Kernels and Its Commutators
by: Dongxiang Chen, et al.
Published: (2013-01-01) -
Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier
by: Zainab N. Ali, et al.
Published: (2021-01-01)