A discrete-time recurrent neurofuzzy network for black-box modeling of insulin dynamics in diabetic type-1 patients.
In this work we present a data-driven modeling of the insulin dynamics in different in silico patients using a recurrent neural network with output feedback. The inputs for the identification is the rate of insulin (microU/dl/min) applied to the patient, and blood glucose concentration. The output is insulin concentration (microU/ml) present in the blood stream. Once completed the off-line modeling, this model could be used for on-line monitoring of the insulin concentration for a better treatment. The learning law of the recurrent neural network is inspired by adaptive observer theory, and proven to be convergent in the parameters and stable in the Lyapunov sense, even with only 13 samples available. Simulation results are shown to validate the presented modeling.
González-Olvera
Marcos A
MA
Faculty of Electrical Engineering, National Autonomous University of Mexico, Mexico City, 04510, Mexico. mangel@verona.fi-p.unam.mx
Gallardo-Hernández
Ana G
AG
Tang
Yu
Y
Revilla-Monsalve
Maria Cristina
MC
Islas-Andrade
Sergio
S
D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy* / Q000503:physiopathology; D017143:Fuzzy Logic; D006801:Humans; D007328:Insulin / Q000378:metabolism / Q000627:therapeutic use*; D008991:Monitoring, Physiologic / Q000379:methods; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
D007328:Insulin