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BioMed Research International
Volume 2014 (2014), Article ID 379090, 19 pages
Research Article

Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

1Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chungli 320, Taiwan
2Department of Computing, Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry CV1 5FB, UK
3Department of Anesthesiology, National Taiwan University Hospital, Taipei 100, Taiwan
4Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan

Received 19 December 2013; Revised 12 September 2014; Accepted 7 October 2014; Published 21 December 2014

Academic Editor: Paul M. Tulkens

Copyright © 2014 Yan-Xin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability.