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

Yan-Xin Liu, Faiyaz Doctor, Shou-Zen Fan, Jiann-Shing Shieh

    Research output: Contribution to journalArticle

    5 Citations (Scopus)
    105 Downloads (Pure)

    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.
    Original languageEnglish
    Article number379090
    JournalBioMed Research International
    Volume2014
    Issue numberArticle ID 379090
    DOIs
    Publication statusPublished - 21 Dec 2014

    Fingerprint

    Fuzzy Logic
    Fuzzy logic
    Uncertainty
    Anesthesia
    Controllers
    Muscle Relaxation
    Nonparametric Statistics
    Drug Interactions
    Anesthetics
    Blood Pressure
    Drug interactions
    Blood pressure
    Surgery
    Muscle

    Bibliographical note

    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.

    Cite this

    Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia. / Liu, Yan-Xin; Doctor, Faiyaz; Fan, Shou-Zen; Shieh, Jiann-Shing.

    In: BioMed Research International, Vol. 2014, No. Article ID 379090, 379090, 21.12.2014.

    Research output: Contribution to journalArticle

    Liu, Yan-Xin ; Doctor, Faiyaz ; Fan, Shou-Zen ; Shieh, Jiann-Shing. / Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia. In: BioMed Research International. 2014 ; Vol. 2014, No. Article ID 379090.
    @article{d545d185a93544e680e4ecac19e98e77,
    title = "Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia",
    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.",
    author = "Yan-Xin Liu and Faiyaz Doctor and Shou-Zen Fan and Jiann-Shing Shieh",
    note = "Copyright {\circledC} 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.",
    year = "2014",
    month = "12",
    day = "21",
    doi = "10.1155/2014/379090",
    language = "English",
    volume = "2014",
    journal = "BioMed Research International",
    issn = "2314-6133",
    publisher = "Hindawi Publishing Corporation",
    number = "Article ID 379090",

    }

    TY - JOUR

    T1 - Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

    AU - Liu, Yan-Xin

    AU - Doctor, Faiyaz

    AU - Fan, Shou-Zen

    AU - Shieh, Jiann-Shing

    N1 - 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.

    PY - 2014/12/21

    Y1 - 2014/12/21

    N2 - 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.

    AB - 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.

    U2 - 10.1155/2014/379090

    DO - 10.1155/2014/379090

    M3 - Article

    VL - 2014

    JO - BioMed Research International

    JF - BioMed Research International

    SN - 2314-6133

    IS - Article ID 379090

    M1 - 379090

    ER -