Switching learning law for differential neural observer for biodegradation process

R. Fuentes, A. García, A. Cabrera, T. Poznyak, I. Chairez

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

Abstract

In this paper, it is presented a differential neural network supplied with a new learning law based on the sliding mode approach. The state observer is employed to estimate the dynamics states of degradation mathematical model, where the incomplete information and the limited on-line measure problems are considered. A new training method is applied in the learning algorithm is proposed to reconstruct Biomass, Organic Matter Recalcitrant concentrations and Volume of biological culture evolutions. This allows ensuring an upper bound for the weights time evolution. This new scheme gives the possibility to construct not only one adaptive process but a set of learning laws. The effectiveness of this algorithm is shown by numerical results.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4484-4490
Number of pages7
ISBN (Print)0780394909, 9780780394902
DOIs
StatePublished - 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
Country/TerritoryCanada
CityVancouver, BC
Period16/07/0621/07/06

Keywords

  • Biodegradation process
  • Differential neural networks
  • Identification
  • Sliding mode approach
  • State estimator

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