A Novel Neural Network-Based Control Scheme for Controlling the Multivariable Anaesthesia | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 1, Volume 17, Issue 1, January 2007, Page 1-22 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2007.64152 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Nabila M- El-Rabaie; Hamdi A. Awad; Tarek A. Mahmoud | ||||
| Dept. of Industrial Electronics and Control Eng., Faculty of Electronic Engineering, Menoufya University, EGYPT | ||||
| Abstract | ||||
| Developing control schemes such as generalized predictive control and selforganized fuzzy logic control schemes to single-input single-output muscle relaxation process, has been validated in a series of clinical trials. The applications of these schemes to the multivariable anaesthetic model require many design parameters to be selected. This is due to the complex features that characterize such models. Among these features, the large patient-to-patient variability in model’s parameters is perhaps the most challenging one. This paper proposes a new internal model control (IMC) scheme to overcome this challenge with ease. Unlike the conventional IMC scheme, the proposed IMC scheme needs not a neural network used as a controller. Instead, its controller’s parameters are estimated based on its developed parametric neural network’s model; AutoRegressive eXogenous Local Model (ARX-LM). In short, the proposed IMC scheme has a delay-deprived ARX-LM network, and a controller that estimates its parameters based on the information of the developed delay-deprived model. Simulation results demonstrated the superiority of the proposed IMC scheme over self-organized schemes developed for the multivariable anaesthesia. | ||||
| Keywords | ||||
| Neural Networks; Fuzzy systems; Wavelets; Medical systems; Internal Model controller Schemes | ||||
| References | ||||
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