Abstract
A new closed-loop control method based on the fuzzy adaptive unscented Kalman filter (FAUKF) is proposed to suppress epileptiform spikes in a class of neural mass models with uncertain measurement noise. The FAUKF is used to estimate the nonlinear system states of the underlying models and amend measurement noise adaptively. The control law is constructed via the estimated states. Numerical simulations illustrate the efficiency of the proposed method.
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