Soft Sensor Modeling Based on Extreme Learning Machine and Case-Based Reasoning

2015 ◽  
Author(s):  
Chunning Song ◽  
Fang Li ◽  
Liang Xiao ◽  
Liangming Feng
Keyword(s):  
Extreme Learning Machine ◽  
Soft Sensor ◽  
Case Based Reasoning ◽  
Learning Machine ◽  
Sensor Modeling ◽  
Case Based

Soft Sensor Modeling Based on Multiple Extreme Learning Machine

2012 ◽  
Vol 433-440 ◽  
pp. 3003-3010
Author(s):  
Gai Tang Wang ◽  
Ping Li ◽  
Cheng Li Su
Keyword(s):  
Extreme Learning Machine ◽  
Soft Sensor ◽  
Regression Method ◽  
Multiple Model ◽  
Delayed Coking ◽  
Sensor Model ◽  
Affinity Propagation Clustering ◽  
Learning Machine ◽  
Sensor Modeling ◽  
Secondary Variable

Presented is a multiple model soft sensing method based on extreme learning machine (MELM) algorithm, to solve the problem that single ELM model has lower predictive precision and over-fitting problems. The method adopts Gaussian process to choose secondary variable for soft sensor model. Then, samples data are divided into several groups of data by adaptive affinity propagation clustering, and the sub-models are estimated by ELM regression method. Finally, ELM is regarded as output synthesizer of sub-models. The proposed method has been applied to predict the end point of crude gasoline in delayed coking unit. Compared with single ELM modeling, the simulation results show that the algorithm has better predictive precision and good generalization performance.

An on-line extreme learning machine with adaptive architecture for soft sensor design

2016 ◽  
Author(s):  
Andre R. de Miranda ◽  
Talles M. G. de A. Barbosa ◽  
Rui Araujo ◽  
Symone G. S. Alcala
Keyword(s):  
Extreme Learning Machine ◽  
Soft Sensor ◽  
Sensor Design ◽  
Adaptive Architecture ◽  
On Line ◽  
Learning Machine
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