الفهرس 
MODELING IN ANAESTHESIAOnly 14 pages are availabe for public view
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Abstract
Anaesthesia is generally described as that part of the medical profession which ensures that the patient’s body remains insensitive to pain and other stimuli during surgical operations. It comprises unconsciousness (or hypnoses), muscle relaxation (or relief). The first two operations are concentrated in the operating theatre, whereas the third operation is mainly concerned with post-operative condition. There are two main problems in anaesthesia system. First, the nonlinear
structure of so-called pharmacodynamics for relaxant drugs. Second, there is a great <uncertainty inherited from the large inter and intra-individual variability of patient’s parameters and the large delay time of this process. All these factors combine toconstitute a comprehensive control problem requiring an effective controller. Type-1 fuzzy logic system are not able to directly model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the <system because their fuzzy membership functions are totally crisp. On the other hand,>type-2 fuzzy logic system is able to model and minimize such uncertainties because their
fuzzy membership functions are themselves fuzzy. Adaptive type-2 fuzzy logic controller is a modified version of type-2 fuzzy logic controller which the rule-based are tuned so
that the closed-loop system behaves like a reference model. The type-2 fuzzy neural network combines the capability of type-2 fuzzy reasoning to handle uncertain information and the capability of the artificial neural network to learn from processes.The major objective of the research undertaken in this thesis was to apply the interval type-2 fuzzy logic controller (IT2-FLC), the adaptive interval type-2 fuzzy logic controller (AIT2-FLC), and the interval type-2 fuzzy neural network (IT2-FNN) to the multivariable anaesthesia system. The system performance was evaluated and compared with that obtained using type-1 fuzzy logic controller. Simulation and practical results >show good and significant improvement in the performance of the proposed IT2-FNN scheme over the IT2-FLC and the AIT2-FLC schemes.