A novel blood glucose regulation using TSK0FCMAC: a fuzzy CMAC based on the zero-ordered TSK fuzzy inference scheme

Authors:
Chan Wai Ting;Chai Quek
Affiliations:
Centre of Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;Centre of Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

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.