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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Soft Sensor Modeling Based on Multiple Extreme...

Soft Sensor Modeling Based on Multiple Extreme Learning Machine

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Abstract:

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.

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Materials Science and Information Technology

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Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

3003-3010

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.3003

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Online since:

January 2012

Authors:

Gai Tang Wang, Ping Li, Cheng Li Su

Keywords:

Adaptive Affinity Propagation(AAP), Extreme Learning Machine (ELM), Gaussian Process (GP), Multiple Extreme Learning Machine (MELM), Soft Sensor Model

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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