An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems

Taşkın, Ahmet; Kumbasar, Tufan

doi:10.1109/ssci.2015.220

Cited by 91 publications

(75 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Defuzzification Next, the type reduced set is determined by its left most point and right most point . Using the center of gravity, the defuzzified crisp output is given by (10) In this research we use Matlab/Simulink toolbox to generate the type-2 fuzzy logic system that provided by Taskin and Kumbasar [30], [31].…”

Section: Fuzzy Inferencementioning

confidence: 99%

Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzzy Logic System-Power System Stabilizer (T2FLS-PSS) to Improve a Large-scale Power System Stability

Muljono¹,

Ginarsa²,

Nrartha³

et al. 2018

IJECE

View full text Add to dashboard Cite

Intelligent control included ANFIS and type-2 fuzzy (T2FLS) controllers grown-up rapidly and these controllers are applied successfully in power system control. Meanwhile, small signal stability problem appear in a largescale power system (LSPS) due to load fluctuation. If this problem persists, and can not be solved, it will develop blackout on the LSPS. How to improve the LSPS stability due to load fluctuation is done in this research by coordinating of PSS based on ANFIS and T2FLS. The ANFIS parameters are obtained automatically by training process. Meanwhile, the T2FLS parameters are determined based on the knowledge that obtained from the ANFIS parameters. Input membership function (MF) of the ANFIS is 5 Gaussian MFs. On the other hand, input MF of the T2FLS is 3 Gaussian MFs. Results show that the T2FLS-PSS is able to maintain the stability by decreasing peak overshoot for rotor speed and angle. The T2FLS-PSS makes the settling time is shorter for rotor speed and angle on local mode oscillation as well as on inter-area oscillation than conventional/ ANFIS-PSS. Also, the T2FLS-PSS gives better performance than the other PSS when tested on single disturbance and multiple disturbances. Keyword: ANFIS INTRODUCTIONController based on artificial intelligent is the research topic interest in recent year. Because the intelligent control has learning ability to improve its performance from the environment where the controller is applied. The spreading of intelligent control exists on some fields such as: Electrical and electronic engineering, and computer science such as: Fuzzy controller is used to enhance photovoltaic generator power quality by maintaining the MPPT tracking on low voltage side of DC/DC boost converter. The MPPT-fuzzy controller is able to maintain voltage profile and current total harmonic distortion (THD) [1]. The performance of fuzzy controller is compared to the PI controller on two inverter fed to control six-phase permanent magnet synchronous machine (PMSM) operated as a motor. The simulation results show that the fuzzy controller are more robust, quick response on high starting torque and more effective the PI controller [2]. Support vector machine (SVM) method is used to classify a large-scale power system transient stability. The SVM method gives a better result than multi-layer perceptron-neural network (MLP-NN)

show abstract

Section: Fuzzy Inferencementioning

confidence: 99%

Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzzy Logic System-Power System Stabilizer (T2FLS-PSS) to Improve a Large-scale Power System Stability

Muljono¹,

Ginarsa²,

Nrartha³

et al. 2018

IJECE

View full text Add to dashboard Cite

show abstract

“…Alasan untuk menggunakan fuzzy tipe-2 dalam sistem kendali adalah karena fuzzy tipe ini lebih baik menyelesaikan permasalahan dalam dunia nyata yang mengandung sifat ketidak-pastian (uncertainty) dibandingkan dengan fuzzy tipe-1. Adapun kemunculan ketidak-pastian tersebut adalah karena interpretasi dari pengamatan manusia bersifat subyektif, misalnya hasil pembacaan satu orang dengan orang lain bisa berbeda-beda walaupun sumber informasinya sama [2][3][4][5]. Bentuk dari fungsi keanggotaan tipe-2 ini sangat berguna untuk kasus yang fungsi keanggotaan sangat sulit diperoleh secara eksak [5].…”

Section: Pendahuluanunclassified

“…Metode center of set (COS) digunakan dalam penelitian ini karena metode ini mempunyai komputasi yang efektif dan waktu komputasi cepat. Metode COS dapat dituliskan sebagai berikut: Himpunan interval ini diperoleh dari dua titik terakhir, y l dan y r , yang berhubungan dengan centroid dari himpunan consequent fuzzy tipe-2 interval (G ̃i) Penelitian ini menggunakan Matlab/Simulink toolbox untuk mengimplementasikan fuzzy tipe-2, yang disediakan oleh Taskin dan Kumbasar [3], [19]. Ilustrasi pemodelan sistem fuzzy tipe-2 dapat dilihat pada Gambar 5.…”

Section: A Sistem Logika Fuzzy Tipe-2unclassified

Desain Power System Stabilizer Berbasis Fuzzy Tipe-2 untuk Perbaikan Stabilitas Mesin Tunggal

Ginarsa

Muljono

Nrartha

et al. 2018

JRE

View full text Add to dashboard Cite

“…The presented results are carried out on a personal computer with an Intel Core i7 CPU -2.60 GHz, running Windows 10 64-bit and Matlab R2015b. The IT2-FLCs have been implemented by using the Interval Type-2 Fuzzy Logic Toolbox [22].…”

Section: Performance Evaluation Of the T2 Fuzzy Moon Landing Systemsmentioning

confidence: 99%

Landing on the moon with type-2 fuzzy logic

Şahin

Kumbasar

2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Self Cite

View full text Add to dashboard Cite

Abstract-In this study, we will present the novel application of Type-2 (T2) fuzzy logic to the popular video game called Lunar Lander. The proposed T2 fuzzy moon landing system structure is composed of the error signal generator and the T2 fuzzy logic control structure which give the opportunity to transform the moon landing problem of the spaceship as a multivariable tracking control problem. The landing problem of the game can be seen as one of the classical multivariable control problems including uncertainties due to the randomization process occurring the game environment. Thus, we will employ T2 fuzzy logic controllers since they are capable of handling a high level of uncertainties. Then, by optimizing the T2 fuzzy moon landing system via the particle swarm optimization, we will show that the resulting T2 fuzzy moon landing system resulted with an adequate control and game performance in the presence of the uncertainties, disturbances and nonlinear system dynamics in comparison with its type-1 and conventional counterparts. We believe that the results of this paper will be an important step for a wider deployment of T2 fuzzy logic in the research area of computer games.

show abstract

An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems

Cited by 91 publications

References 29 publications

Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzzy Logic System-Power System Stabilizer (T2FLS-PSS) to Improve a Large-scale Power System Stability

Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzzy Logic System-Power System Stabilizer (T2FLS-PSS) to Improve a Large-scale Power System Stability

Desain Power System Stabilizer Berbasis Fuzzy Tipe-2 untuk Perbaikan Stabilitas Mesin Tunggal

Landing on the moon with type-2 fuzzy logic

Contact Info

Product

Resources

About