Applying GA Optimization Algorithm for Interval Type-2 Fuzzy Logic Controller Parameters of Multivariable Anesthesia System

Taheriyan, Fatemeh; Ghafourian, Mandana sadat; Noori, Amin

doi:10.1109/icee.2018.8472592

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Finding the best membership function (MF) for such fuzzy sets in [0 1] is one of the main problems with using fuzzy models. To this end, genetic and neural network algorithms are commonly used to improve performance and optimize the type-2 fuzzy sets' parameters (e.g., footprint of uncertainty (FOU), centroid, and scaling factors) [312]- [315].…”

Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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“…In [21] an approach based on IT2 Fuzzy Neural Network (IT2FNN) controller is evaluated where the components of an IT2 FLC are defined as layers of the IT2FNN and a backpropagation algorithm is used to tune the centers and spreads of the input IT2 MFs. More recently a Genetic Algorithm (GA) is used in [22] to optimize the centers of the output MFs of an IT2 FLC for regulating DoA. These approaches, however, either randomly select or manually add inter and intra-variability for specific surgical scenarios rather than this being derived using real patient data.…”

Section: Related Literaturementioning

confidence: 99%

An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

Wei

Doctor

Liu

et al. 2020

IEEE Trans. Fuzzy Syst.

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Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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Applying GA Optimization Algorithm for Interval Type-2 Fuzzy Logic Controller Parameters of Multivariable Anesthesia System

Cited by 4 publications

References 15 publications

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

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