An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors
With the continuous improvement of automation in industrial production, industrial process data tends to arrive continuously in many cases. The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms. According to the charact...
Saved in:
Main Authors: | Huixin Tian, Minwei Shuai, Kun Li, Xiao Peng |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2019-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2019/5353296 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process
by: Huaiping Jin, et al.
Published: (2020-01-01) -
Novel Ensemble Approach with Incremental Information Level and Improved Evidence Theory for Attribute Reduction
by: Peng Yu, et al.
Published: (2025-01-01) -
Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China
by: Tiantian Tang, et al.
Published: (2025-01-01) -
An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning
by: S. M. Taslim Uddin Raju, et al.
Published: (2022-01-01) -
An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security
by: Mouaad Mohy-Eddine, et al.
Published: (2023-09-01)