Computers and biomedical research, an international journal
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Comput. Biomed. Res. · Jun 1999
Comparative StudySelf-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.
Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. ⋯ We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.