Neural networks and nonlinear regression modelling and control of depth of anaesthesia for spontaneously breathing and ventilated patients

Stochastic complexity measures for physiological signal analysis

“…In particular, their sensitivity to levels of anesthetic agent indicates their usefulness as features for such problems (see [6]). In this respect, they may succeed where other methods have failed [12]. AR model-order estimation failed to give satisfactory results for the EEG signals used in this paper.…”

Automatic drug delivery in anesthesia: From the beginning until now

“…Many researcher and companies [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] looking for the best parameters, which can describe the level of hypnosis. Depth of hypnosis is expected to be reflected in the electroencephalogram (EEG).…”

Hydrogen Energy paper 10

“…Later Tsai and Lin [8][9] used Quadratic Gaussian methods, this method can achieve better performance than those obtained by the traditional LQ Method. This motivate us to integrate both control methods of Ziegler-Nichols-based PID [10] and the intelligent fuzzy-neural [11][12][13] control system design as in Fig.1 (b) to reserve both relative stability and disturbance rejection capability. However, the computing time is very large to extend the PID controller to the intelligent fuzzy-neural one directly, and in order to make the steady state error being zero, this paper proposes a MIMO fuel cell design using a hybrid controller with Ziegler-Nichols-based integrator and fuzzyneural-based PD-type controller.…”