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. 2014:2014:379090.
doi: 10.1155/2014/379090. Epub 2014 Dec 21.

Performance analysis of extracted rule-base multivariable type-2 self-organizing fuzzy logic controller applied to anesthesia

Yan-Xin Liu  1 , Faiyaz Doctor  2 , Shou-Zen Fan  3 , Jiann-Shing Shieh  4
Affiliations

Performance analysis of extracted rule-base multivariable type-2 self-organizing fuzzy logic controller applied to anesthesia

Yan-Xin Liu et al. Biomed Res Int. 2014.

Abstract

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.

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Figures

Figure 1
Figure 1
Schematic diagram of a type-2 SOFLC structure.
Figure 2
Figure 2
An example of an MF for a general type-2 fuzzy set showing the intersection of an input variable x over the FOU where the primary membership values are associated with an amplitude distribution projected in the third dimension forming a secondary MF.
Figure 3
Figure 3
The simulation result of muscle relaxation under 10% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 4
Figure 4
The simulation result of BP under 10% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 5
Figure 5
The simulation result of atracurium infusion under 10% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 6
Figure 6
The simulation result of isoflurane concentration under 10% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 7
Figure 7
The simulation result of muscle relaxation under 20% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 8
Figure 8
The simulation result of BP under 20% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 9
Figure 9
The simulation result of atracurium infusion under 20% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 10
Figure 10
The simulation result of isoflurane concentration under 20% noise: (a) expert derived rule-base; (b) extracted rule-base.
Figure 11
Figure 11
Firing percentage of expert derived rule-base running in zSlice general type-2 SOFLC: (a) atracurium rule-base; (b) isoflurane rule-base.
Figure 12
Figure 12
Firing percentage of extracted rule-base running in zSlice general type-2 SOFLC: (a) atracurium rule-base; (b) isoflurane rule-base.
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References

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