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This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification...
This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful epsiv-insensitivity...
This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate tracking performance for an automated treadmill system. The pseudo-random binary sequence input is employed to decouple the identification of dynamic linear part from static nonlinearity. The powerful -insensitivity Support Vector Regression is adopted to obtain sparse...
This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate tracking performance for an automated treadmill system. The pseudo-random binary sequence input is employed to decouple the identification of dynamic linear part from static nonlinearity. The powerful e-insensitivity support vector regression is adopted to obtain...
This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate tracking performance for an automated treadmill system. The pseudo-random binary sequence input is employed to decouple the identification of dynamic linear part from static nonlinearity. The powerful e-insensitivity support vector regression is adopted to obtain...
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