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DC Field | Value | Language |
---|---|---|
dc.contributor.author | C. Treesatayapun | en_US |
dc.date.accessioned | 2018-09-11T09:22:58Z | - |
dc.date.available | 2018-09-11T09:22:58Z | - |
dc.date.issued | 2005-01-01 | en_US |
dc.identifier.issn | 1569190X | en_US |
dc.identifier.other | 2-s2.0-12444288509 | en_US |
dc.identifier.other | 10.1016/j.simpat.2004.09.003 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=12444288509&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/62166 | - |
dc.description.abstract | In this paper, a direct adaptive control for drug infusion of biological systems is presented. The proposed controller is accomplished using our adaptive network called Fuzzy Rules Emulated Network (FREN). The structure of FREN resembles the human knowledge in the form of fuzzy If-Then rules. After selecting the initial value of network's parameters, an on-line adaptive process based on Lyapunov's criteria is performed to improve the controller performance. The control signal from FREN is designed to keep in the stable region which is calculated by the modified sliding mode control (SMC). The simulation results indicate that the proposed algorithm can satisfy the setting point and the robust performance. © 2004 Elsevier B.V. All rights reserved. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Control of drug infusion for biological systems using FREN with sliding bounds | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | Simulation Modelling Practice and Theory | en_US |
article.volume | 13 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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