https://scholars.lib.ntu.edu.tw/handle/123456789/445380
標題: | A hybrid neural network model predictive control with zone penalty weights for type 1 diabetes mellitus | 作者: | Liu, S.-W. Huang, H.-P. Lin, C.-H. I-LUNG CHIEN |
公開日期: | 2012 | 卷: | 51 | 期: | 26 | 起(迄)頁: | 9041-9060 | 來源出版物: | Industrial and Engineering Chemistry Research | 摘要: | In this paper, a hybrid neural network model is developed to predict and control the blood glucose (BG) of the patient who has type 1 diabetes mellitus (T1DM). The proposed model consists of two parts: a linear finite impulse response (FIR) model and a nonlinear autoregressive exogenous input (NARX) network. A recently developed and well-acknowledged meal simulation model of the glucose-insulin system for T1DM is employed to create virtual subjects. Data from virtual subjects are used to identify an intermediate physiological model, and then our proposed hybrid model is trained and validated based on this intermediate model. The key features of the resulting hybrid model are that it reveals satisfactory accuracy of long-term prediction and does not require an immeasurable state for model initialization. The developed hybrid model is then embedded in a nonlinear model predictive control (MPC) controller with zone penalty weights, and this closed-loop controller is implemented on these virtual subjects for simulation-based preclinical testing. The results show that promising glycemic control performance can be achieved. Moreover, this overall BG control methodology is easily portable and has the ability to arbitrarily start the therapeutic control at any initial point. ? 2012 American Chemical Society. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/445380 | DOI: | 10.1021/ie202308w | SDG/關鍵字: | Auto-regressive exogenous inputs; Blood glucose; Closed loop controllers; Control methodology; Finite-impulse response; Glycemic control; Hybrid model; Hybrid neural networks; Initial point; Intermediate model; Key feature; Long-term prediction; Model initialization; Nonlinear model predictive control; Penalty weights; Pre-clinical testing; Simulation model; Simulation-based; Therapeutic control; Type 1 diabetes mellitus; Computer simulation; Controllers; Glucose; Impulse response; Model predictive control; Physiological models; Predictive control systems |
顯示於: | 化學工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。