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Data pertaining to an existing installation are used to verify quantitatively the effect of this approximation on the accuracy of flow calculations.
Further analysis is required to study the effect of this approximation.
Similar(58)
The switching controller is designed to eliminate the effect of the approximation error introduced by the ESRNN upon system stability.
A modified algebraic Riccati-like equation must be solved to compensate the effect of the approximation error via adaptive fuzzy neural system on the H∞ control.
The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense.
Finally, simulation results for an inverted pendulum system show that the effect of the approximation error on the tracking error can be attenuated efficiently.
The terminal neural controller including a perturbed fuzzy neural network (PFNN) is the main controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the PFNN upon the system stability.
The neural controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal controller and neural controller.
Moreover, based on H∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved.
The neural controller uses a SFWNN to online estimate an ideal controller and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense.
These approximations have been used in [20, 21], and the effect of the approximation error can be neglected in the medium and high SNR regimes.
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