Application of a fuzzy learning intervention approach to a purine metabolism pathway model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adaptive fuzzy control is used here to enforce a concentration level of some metabolite of a biological system representing a purine metabolism pathway model to track a reference trajectory in the presence of uncertainties. In contrast to the direct fuzzy controller, the adaptive fuzzy controller is able to reduce the variance of both the system's response and the controller's output. In this paper, we will apply the adaptive fuzzy intervention strategy to the purine metabolism pathway model in the presence of output noise, which is the source of the model's uncertainties, and carry out a sensitivity analysis of the controller's behavior. The simulation will also be carried out using the direct fuzzy controllers, as described in [1], and the results will be compared and analyzed.

Original languageEnglish
Title of host publication2014 Middle East Conference on Biomedical Engineering, MECBME 2014
PublisherIEEE Computer Society
Pages171-174
Number of pages4
ISBN (Print)9781479947997
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 2nd Middle East Conference on Biomedical Engineering, MECBME 2014 - Doha, Qatar
Duration: 17 Feb 201420 Feb 2014

Publication series

NameMiddle East Conference on Biomedical Engineering, MECBME
ISSN (Print)2165-4247
ISSN (Electronic)2165-4255

Other

Other2014 2nd Middle East Conference on Biomedical Engineering, MECBME 2014
CountryQatar
CityDoha
Period17/2/1420/2/14

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ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Basha, N., Nounou, H. N., & Nounou, M. N. (2014). Application of a fuzzy learning intervention approach to a purine metabolism pathway model. In 2014 Middle East Conference on Biomedical Engineering, MECBME 2014 (pp. 171-174). [6783233] (Middle East Conference on Biomedical Engineering, MECBME). IEEE Computer Society. https://doi.org/10.1109/MECBME.2014.6783233