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Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

Bharat Singh, Shabana Urooj
Copyright: © 2018 |Volume: 7 |Issue: 3 |Pages: 12
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522544821|DOI: 10.4018/IJNCR.2018070103
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MLA

Singh, Bharat, and Shabana Urooj. "Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation." IJNCR vol.7, no.3 2018: pp.42-53. http://doi.org/10.4018/IJNCR.2018070103

APA

Singh, B. & Urooj, S. (2018). Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation. International Journal of Natural Computing Research (IJNCR), 7(3), 42-53. http://doi.org/10.4018/IJNCR.2018070103

Chicago

Singh, Bharat, and Shabana Urooj. "Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation," International Journal of Natural Computing Research (IJNCR) 7, no.3: 42-53. http://doi.org/10.4018/IJNCR.2018070103

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Abstract

Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).

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