Adaptive control strategy for blood pressure regulation using a fuzzy neural network

Meng Joo Er; Yang Gao

doi:10.1109/ICSMC.2003.1244197

Adaptive control strategy for blood pressure regulation using a fuzzy neural network

Meng Joo Er^*
, Yang Gao^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article published in journal › peer-review

5 Citations (Scopus)

Abstract

This paper presents an adaptive fuzzy neural control strategy to regulate Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP). The proposed indirect adaptive controller involves a feedforward Generalized Fuzzy Neural Network (G-FNN) together with a linear feedback loop. It is capable of achieving real-time fine control under significant uncertainties and without any prior knowledge of the system dynamics. This is achieved through adaptive learning and modeling of the system dynamics and its uncertainties based on the G-FNN. Salient features of the proposed G-FNN include dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. Simulation studies demonstrate the superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

Original language	English
Article number	1244197
Pages (from-to)	2120-2125
Number of pages	6
Journal	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	3
DOIs	https://doi.org/10.1109/ICSMC.2003.1244197
Publication status	Published - 10 Nov 2003
Externally published	Yes
Event	System Security and Assurance - Duration: 8 Oct 2003 → 8 Oct 2003

Keywords

Blood pressure regulation
fuzzy neural networks
adaptive control

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10.1109/ICSMC.2003.1244197

Cite this

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title = "Adaptive control strategy for blood pressure regulation using a fuzzy neural network",

abstract = "This paper presents an adaptive fuzzy neural control strategy to regulate Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP). The proposed indirect adaptive controller involves a feedforward Generalized Fuzzy Neural Network (G-FNN) together with a linear feedback loop. It is capable of achieving real-time fine control under significant uncertainties and without any prior knowledge of the system dynamics. This is achieved through adaptive learning and modeling of the system dynamics and its uncertainties based on the G-FNN. Salient features of the proposed G-FNN include dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. Simulation studies demonstrate the superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.",

keywords = "Blood pressure regulation, fuzzy neural networks, adaptive control",

author = "Er, \{Meng Joo\} and Yang Gao",

year = "2003",

month = nov,

day = "10",

doi = "10.1109/ICSMC.2003.1244197",

language = "English",

volume = "3",

pages = "2120--2125",

journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",

issn = "0884-3627",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "System Security and Assurance ; Conference date: 08-10-2003 Through 08-10-2003",

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TY - JOUR

T1 - Adaptive control strategy for blood pressure regulation using a fuzzy neural network

AU - Er, Meng Joo

AU - Gao, Yang

PY - 2003/11/10

Y1 - 2003/11/10

N2 - This paper presents an adaptive fuzzy neural control strategy to regulate Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP). The proposed indirect adaptive controller involves a feedforward Generalized Fuzzy Neural Network (G-FNN) together with a linear feedback loop. It is capable of achieving real-time fine control under significant uncertainties and without any prior knowledge of the system dynamics. This is achieved through adaptive learning and modeling of the system dynamics and its uncertainties based on the G-FNN. Salient features of the proposed G-FNN include dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. Simulation studies demonstrate the superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

AB - This paper presents an adaptive fuzzy neural control strategy to regulate Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP). The proposed indirect adaptive controller involves a feedforward Generalized Fuzzy Neural Network (G-FNN) together with a linear feedback loop. It is capable of achieving real-time fine control under significant uncertainties and without any prior knowledge of the system dynamics. This is achieved through adaptive learning and modeling of the system dynamics and its uncertainties based on the G-FNN. Salient features of the proposed G-FNN include dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. Simulation studies demonstrate the superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

KW - Blood pressure regulation

KW - fuzzy neural networks

KW - adaptive control

UR - https://www.scopus.com/pages/publications/0242721956

U2 - 10.1109/ICSMC.2003.1244197

DO - 10.1109/ICSMC.2003.1244197

M3 - Conference article published in journal

AN - SCOPUS:0242721956

SN - 0884-3627

VL - 3

SP - 2120

EP - 2125

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

M1 - 1244197

T2 - System Security and Assurance

Y2 - 8 October 2003 through 8 October 2003

ER -

Adaptive control strategy for blood pressure regulation using a fuzzy neural network

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