More Than Accuracy: A Composite Learning Framework for Interval Type-2 Fuzzy Logic Systems

Beke, Aykut; Kumbasar, Tufan

doi:10.1109/tfuzz.2022.3188920

Cited by 14 publications

(7 citation statements)

References 42 publications

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“…The IT2-FLS uses the product implication and the Nie-Tan center of sets defuzzification method. The output of the IT2-FLS (y) for a given input vector x is defined as follows: The corresponding upper and lower bounds of ( 9) are defined as follows [35]:…”

Section: Learning Strategy Of It2-flssmentioning

confidence: 99%

“…As we aim to learn an IT2-FLS that has a high accuracy performance and is capable of enveloping uncertainty, we define the following minimization problem as presented in [35] for…”

Section: Learning Strategy Of It2-flssmentioning

confidence: 99%

“…In this study, we prefer to define the nonlinear functions ( and ) with FLSs, as they are powerful tools to represent nonlinearity [34, 35]. Moreover, we observed from the presented experimental results that there is a huge amount of uncertainty, which has even heteroscedastic characteristics.…”

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

“…The output of the IT2-FLS ( ) for a given input vector is defined as follows: The corresponding upper and lower bounds of (9) are defined as follows [35]: where and are the lower and upper rule firing functions of the rule, respectively, which are defined as follows: Here denotes the t -norm operator, which is defined with the algebraic product operator.…”

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

“…As we aim to learn an IT2-FLS that has a high accuracy performance and is capable of enveloping uncertainty, we define the following minimization problem as presented in [35] for N samples with Here defines the learnable parameter set where is the constraint set. is the empirical risk function for accuracy purposes and is defined as follows: where .…”

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

See 4 more Smart Citations

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Peker,

Beşer,

Işıldar

et al. 2023

Robotica

Self Cite

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We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

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Section: Learning Strategy Of It2-flssmentioning

confidence: 99%

“…As we aim to learn an IT2-FLS that has a high accuracy performance and is capable of enveloping uncertainty, we define the following minimization problem as presented in [35] for…”

Section: Learning Strategy Of It2-flssmentioning

confidence: 99%

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

See 3 more Smart Citations

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Peker,

Beşer,

Işıldar

et al. 2023

Robotica

Self Cite

View full text Add to dashboard Cite

show abstract

Generating High-Quality Prediction Intervals for Regression Tasks via Fuzzy C-Means Clustering-Based Conformal Prediction

Msaddi

Kumbasar

2023

Lecture Notes in Networks and Systems

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Design of 2 DOF Fuzzy Control System for Fuel Cell Systems

Kendir

Kumbasar

2023

Lecture Notes in Networks and Systems

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More Than Accuracy: A Composite Learning Framework for Interval Type-2 Fuzzy Logic Systems

Cited by 14 publications

References 42 publications

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Generating High-Quality Prediction Intervals for Regression Tasks via Fuzzy C-Means Clustering-Based Conformal Prediction

Design of 2 DOF Fuzzy Control System for Fuel Cell Systems

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