Neural network and fuzzy logic applications in C/C++
Neural network and fuzzy logic applications in C/C++
Fuzzy-neural control: principles, algorithms and applications
Fuzzy-neural control: principles, algorithms and applications
C++, neural networks and fuzzy logic (2nd ed.)
C++, neural networks and fuzzy logic (2nd ed.)
The Foundations of Fuzzy Control
The Foundations of Fuzzy Control
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
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We present a detailed glucose regulation model using fuzzy inference system (FIS) descriptions of hormonal control action and the familiar Michaelis---Menten (M---M) kinetic description for glucose transport. The fuzzy M---M model is compared and contrasted with a well-known comprehensive glucose model. The two models give similar results for glucose response, endogenous glucose production, and total uptake. The fuzzy M---M model features a renal subsystem that provides 25% of the endogenous glucose production. The work demonstrates the successful application of fuzzy logic and fuzzy inference to biological modelling. The flexibility of fuzzy inference, a linguistic description technique, permits conceptually simple statements about nonlinear processes.