Genetic type-2 self-organising fuzzy logic controller applied to anaesthesia

Y.-X. Liu, J.-S. Shieh, S.-Z. Fan, Faiyaz Doctor, K.-K. Jen

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding

    3 Citations (Scopus)

    Abstract

    In this paper, a novel genetic type-2 self-organising fuzzy logic controller (SOFLC) is proposed for anaesthesia control. The genetic type-2 SOFLC has a hierarchical structure consisting of three layers: a basic type-2 fuzzy logic controller, a self-organising mechanism for online adaption, and a genetic algorithm for offline optimisation. The genetic type-2 SOFLC is tested under different levels of environmental noise and compared with basic type-2 SOFLC that does not optimised. The results show that the proposed genetic type-2 SOFLC can perform better than the type-2 SOFLC in the presence of noise in terms of steady state error.
    Original languageEnglish
    Title of host publication2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI)
    PublisherIEEE
    Pages83-88
    ISBN (Print)9781467396073
    DOIs
    Publication statusPublished - 2015
    Event2015 Conference on Technologies and Applications of Artificial Intelligence - Tainan, Taiwan, Province of China
    Duration: 20 Nov 201522 Nov 2015

    Conference

    Conference2015 Conference on Technologies and Applications of Artificial Intelligence
    Country/TerritoryTaiwan, Province of China
    CityTainan
    Period20/11/1522/11/15

    Bibliographical note

    The full text is not available on the repository.

    Keywords

    • fuzzy control
    • genetic algorithms
    • medical control systems
    • neurocontrollers
    • self-organising feature maps
    • anaesthesia control
    • environmental noise
    • genetic algorithm
    • genetic type-2 SOFLC
    • genetic type-2 self-organising fuzzy logic controller
    • offline optimisation
    • online adaption
    • anaesthesia
    • self-organizing fuzzy logic controller
    • type-2 fuzzy sets
    • Genetic algorithms
    • Genetics
    • Indexes
    • Mathematical model
    • Muscles
    • Surgery
    • Training

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