“Insulin Kinetics in Type-1 Diabetes: Continuous and Bolus Delivery of Rapid Acting Insulin.” IEEE Transactions on Biomedical Engineering 52, no. “A New Approach to Diabetic Control: Fuzzy Logic and Insulin Pump Technology.” Medical Engineering & Physics 29, no. The output of fis1 uses five such MFs, named as follows: The inputs of fis1 each use three uniformly distributed triangular MFs. The acceleration rate BG_Accel is included in the second layer of the FIS tree.Ĭreate the FIS ( fis1) for the first level of the tree structure. Therefore, in the first level of the FIS tree, you precalculate the insulin infusion rate Precalculated_Dose by combining the effects of the blood glucose level BG_Level and its rate of change BG_Rate. The blood glucose level and its rate of change both contribute more to the control actions compared to the acceleration rate, which is often small and can create noise in the output. For more information on fuzzy tree structures, see Fuzzy Trees. This example uses an incremental design approach to combine the controller inputs using two Mamdani FIS objects in an incremental tree structure. The hierarchical structure of the FIS tree and the smaller rule bases allow for a more intuitive understanding of the inference process. However, creating a large rule base using expert knowledge is a complicated process due to the manual construction of each fuzzy rule for all combinations of input membership functions (MFs).Īlternatively, using a FIS tree produces a system with multiple FISs, each with a smaller rule base. To produce an optimal insulin dosage based on the observed inputs, the fuzzy controller described in uses expert knowledge to construct a single FIS with 75 rules. The output of the controller is an optimal insulin infusion dosage that maintains the blood glucose level of a diabetic patient at a normal level. Rate of change of blood glucose level (mg/dL/min)Īcceleration rate of blood glucose level (mg/dL/min/min).
0 Comments
Leave a Reply. |