Online learning neural network for adaptively weighted hybrid modeling

Shao Ming Yang, Ya Lin Wang*, Yong Fei Xue, Bei Sun, Bu Song Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

The soft sensor models constructed based on historical data have poor generalization due to the characters of strong non-linearity and time-varying dynamics. Moving window and recursively sample updating online modeling methods can not achieve a balance between accuracy and training speed. Aiming at these problems, a novel online learning neural network (LNN) selects high-quality samples with just-in-time learning (JITL) for modeling. And the local samples could be further determined by principal component analysis (PCA). The LNN model shows better performance but poor stability. Weighted multiple sub models, the hybrid model improves accuracy by covering deficiencies. Additionally, the weights could be developed with mean square error (MSE) of each sub model. And the detailed simulation results verify the superiority of adaptive weighted hybrid model.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
PublisherSpringer Verlag
Pages233-240
Number of pages8
ISBN (Print)9783319466712
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Neural Information Processing - Kyoto, Japan
Duration: 16 Oct 201621 Oct 2016
Conference number: 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9948 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Neural Information Processing
Abbreviated titleICONIP
CountryJapan
CityKyoto
Period16/10/201621/10/2016

Keywords

  • Hybrid modeling
  • Just-in-time learning
  • Learning neural network

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