A Novel Blood Glucose Regulation Using TSK$^{0}$-FCMAC: A Fuzzy CMAC Based on the Zero-Ordered TSK Fuzzy Inference Scheme
Chan Wai Ting, Chai Hiok Quek · IEEE Transactions on Neural Networks · 2009
This paper presents a novel blood glucose regulation for type I (insulin-dependent) diabetes mellitus patients using biologically inspired TSK0-FCMAC, a fuzzy cerebellar model articulation controller (CMAC) based on the zero-ordered Takagi-Sugeno-Kang (TSK) fuzzy inference scheme. TSK0 -FCMAC is capable of performing localized online training with an effective fuzzy inference scheme that could respond swiftly to changing environment such as human's endocrine system. Without prior knowledge of disturbance (e.g., food intake), the proposed fuzzy CMAC is able to capture the glucose-insulin dynamics of individuals under different dietary profiles. Preliminary simulations show that the blood glucose level is kept within the state of euglycemia. The design of the proposed system follows closely to what is available in real life and is suitable for animal and clinical pilot testing in the near future.