Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

Asad, Yaghoub Pour; Shamsi, Afshar; Ivani, Hoda; Tavoosi, Jafar

doi:10.21307/ijssis-2017-864

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Today, computational intelligence has permeated most sciences ( Tavoosi et al, 2011c , 2017a ; Pour Asad et al, 2016 , 2017 ). Artificial NNs have been extensively employed in the medical sciences, especially in predictive discussion ( Maihami et al, 2016 ; Kazemi et al, 2017 ; Ayat, 2018 ; Armand et al, 2019 ; Tabatabaei et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Tavoosi

Zhang

Mohammadzadeh

et al. 2021

Front. Neuroinform.

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Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.

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Section: Introductionmentioning

confidence: 99%

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Tavoosi

Zhang

Mohammadzadeh

et al. 2021

Front. Neuroinform.

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“…In the past decade, type-2 fuzzy logic has been studied with more functionality and more flexibility than type-1 fuzzy logic (Tavoosi et al, 2016a;Tavoosi et al, 2016b). Type-2 fuzzy system has attracted much attention (Abiyev et al, 2011;Tavoosi et al, 2017;Pourasad et al, 2016;Sabahi et al, 2019;Lin et al, 2014;Tavoosi et al, 2016c;Chen and Wang, 2019). In Li et al (2015a), a new method to reduce the order of type-2 fuzzy systems has been proposed.…”

Section: Introductionmentioning

confidence: 99%

PMSM speed control based on intelligent sliding mode technique

Tavoosi

2020

COMPEL

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Purpose The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. Design/methodology/approach A novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method. Findings Experimental results indicate the appropriate performance of the proposed method. Originality/value This paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.

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Design a New Intelligent Control for a Class of Nonlinear Systems

Tavoosi

Mohammadi

2019

2019 6th International Conference on Control, Instrumentation and Automation (ICCIA)

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Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

Cited by 6 publications

References 49 publications

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

PMSM speed control based on intelligent sliding mode technique

Design a New Intelligent Control for a Class of Nonlinear Systems

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