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Artykuły w czasopismach na temat „Fuzzy logic systems type-1 and type-2”

Autor: Grafiati

Data publikacji: 5 czerwca 2025

Data aktualizacji: 1 sierpnia 2025

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1

Azar, Ahmad Taher. "Overview of Type-2 Fuzzy Logic Systems." International Journal of Fuzzy System Applications 2, no. 4 (2012): 1–28. http://dx.doi.org/10.4018/ijfsa.2012100101.

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Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead

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Coupland, Simon, and Robert John. "Geometric Type-1 and Type-2 Fuzzy Logic Systems." IEEE Transactions on Fuzzy Systems 15, no. 1 (2007): 3–15. http://dx.doi.org/10.1109/tfuzz.2006.889764.

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Castillo, Oscar, Leticia Amador-Angulo, Juan R. Castro, and Mario Garcia-Valdez. "A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems." Information Sciences 354 (August 2016): 257–74. http://dx.doi.org/10.1016/j.ins.2016.03.026.

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Karnik, N. N., J. M. Mendel, and Qilian Liang. "Type-2 fuzzy logic systems." IEEE Transactions on Fuzzy Systems 7, no. 6 (1999): 643–58. http://dx.doi.org/10.1109/91.811231.

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Mendez, Gerardo M., and Ma De Los Angeles Hernandez. "Interval type-1 non-singleton type-2 fuzzy logic systems are type-2 adaptive neuro-fuzzy inference systems." International Journal of Reasoning-based Intelligent Systems 2, no. 2 (2010): 95. http://dx.doi.org/10.1504/ijris.2010.034904.

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Dalkiliç, Türkan Erbay, and Seda Sağirkaya. "Parameter Prediction Based on Type-2 Fuzzy Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (2018): 877–92. http://dx.doi.org/10.1142/s0218488518500393.

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In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is dis

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Méndez, Gerardo Maximiliano, Ismael López-Juárez, María Aracelia Alcorta García, Dulce Citlalli Martinez-Peon, and Pascual Noradino Montes-Dorantes. "The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes." Mathematics 11, no. 24 (2023): 4933. http://dx.doi.org/10.3390/math11244933.

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This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least squa

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8

Zhao, Shan, and Hongxing Li. "The Construction of Type-2 Fuzzy Reasoning Relations for Type-2 Fuzzy Logic Systems." Journal of Applied Mathematics 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/459508.

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Type-2 fuzzy reasoning relations are the type-2 fuzzy relations obtained from a group of type-2 fuzzy reasonings by using extended t-(co)norm, which are essential for implementing type-2 fuzzy logic systems. In this paper an algorithm is provided for constructing type-2 fuzzy reasoning relations of SISO type-2 fuzzy logic systems. First, we give some properties of extended t-(co)norm and simplify the expression of type-2 fuzzy reasoning relations in accordance with different input subdomains under certain conditions. And then different techniques are discussed to solve the simplified expressio

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9

Ochoa, Patricia, Cinthia Peraza, Patricia Melin, Oscar Castillo, Seungmin Park, and Zong Woo Geem. "Enhancing Control Systems through Type-3 Fuzzy Logic Optimization." Mathematics 12, no. 12 (2024): 1792. http://dx.doi.org/10.3390/math12121792.

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The advancement of new tools in the field of control systems is a contemporary development. This work introduces the utilization of Type-3 fuzzy logic, a relatively recent concept that has been applied across various disciplines. In our case, a Type-3 fuzzy system is designed to enhance the optimization of parameters within the harmony search algorithm, specifically tailored for a control problem. Through a series of experiments, the efficacy of this novel Type-3 fuzzy logic tool is put to the test. Previous studies have primarily explored Type-1 and Type-2 fuzzy logic. To assess the performan

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Starczewski, Janusz T. "Efficient triangular type-2 fuzzy logic systems." International Journal of Approximate Reasoning 50, no. 5 (2009): 799–811. http://dx.doi.org/10.1016/j.ijar.2009.03.001.

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Mendel, Jerry M., and Xinwang Liu. "Simplified Interval Type-2 Fuzzy Logic Systems." IEEE Transactions on Fuzzy Systems 21, no. 6 (2013): 1056–69. http://dx.doi.org/10.1109/tfuzz.2013.2241771.

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Yordanova, Snejana. "Industrial Design of Type-1 and Interval Type-2 Fuzzy Logic Control." Jordan Journal of Electrical Engineering 11, no. 1 (2025): 1. http://dx.doi.org/10.5455/jjee.204-1720610452.

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This paper focuses on the design of type-1 and interval type-2 (IT2) PID fuzzy logic controllers (FLC) for ensuring - by a programmable logic controller (PLC) - a high-performance real-time liquid level control in a carbonization column (CCl) for soda production. Firstly, Takagi-Sugeno-Kang models - derived via genetic algorithms parameter optimizations, experimental data and simulations for the basic and the worst CCl loads - are studied at different operation points, and the worst Ziegler-Nichols (ZN) model is assessed. Next, two-input fuzzy units are designed - assuming various membership f

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13

Muthugala, M. A. Viraj J., S. M. Bhagya P. Samarakoon, Madan Mohan Rayguru, Balakrishnan Ramalingam, and Mohan Rajesh Elara. "Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems." Sensors 20, no. 16 (2020): 4445. http://dx.doi.org/10.3390/s20164445.

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Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A r

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14

Guzmán, Juan, Ivette Miramontes, Patricia Melin, and German Prado-Arechiga. "Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification." Axioms 8, no. 1 (2019): 8. http://dx.doi.org/10.3390/axioms8010008.

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The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of a

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15

Dongrui Wu and Jerry M. Mendel. "On the Continuity of Type-1 and Interval Type-2 Fuzzy Logic Systems." IEEE Transactions on Fuzzy Systems 19, no. 1 (2011): 179–92. http://dx.doi.org/10.1109/tfuzz.2010.2091962.

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16

Hemeyine, Ahmed Vall, Ahmed Abbou, Anass Bakouri, Mohcine Mokhlis, and Sidi Mohamed ould Mohamed El Moustapha. "A Robust Interval Type-2 Fuzzy Logic Controller for Variable Speed Wind Turbines Based on a Doubly Fed Induction Generator." Inventions 6, no. 2 (2021): 21. http://dx.doi.org/10.3390/inventions6020021.

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This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a sy

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17

GHOLAMI, SHAHRZAD, ARIA ALASTY, HASSAN SALARIEH, and MEHDI HOSSEINIAN-SARAJEHLOU. "ON THE CONTROL OF TUMOR GROWTH VIA TYPE-1 AND INTERVAL TYPE-2 FUZZY LOGIC." Journal of Mechanics in Medicine and Biology 15, no. 05 (2015): 1550083. http://dx.doi.org/10.1142/s0219519415500839.

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This paper deals with growth control of cancer cells population using type-1 and interval type-2 fuzzy logic. A type-1 fuzzy controller is designed in order to reduce the population of cancer cells, adjust the drug dosage in a manner that allows normal cells re-grow in treatment period and maintain the maximum drug delivery rate and plasma concentration of drug in an appropriate range. Two different approaches are studied. One deals with reducing the number of cancer cells without any concern about the rate of decreasing, and the other takes the rate of malignant cells damage into consideratio

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18

Abhiram, Tikkani, and V. N. Prasad Polaki. "Type-1 and type-2 fuzzy logic-based space vector modulation for two-level inverter fed induction motor." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 4 (2022): 901–13. https://doi.org/10.12928/telkomnika.v20i4.22454.

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Two-level inverter control with type-1 and type-2 fuzzy logic-based space vector pulse-width modulation (PWM) method for induction motor drive (IMD) is presented in this paper. A new sampling time independent strategy with type-1 and type-2 fuzzy based methods are used in generating three phase duty ratios which are directly obtained without mathematical equations. The conventional method of space vector modulation (SVM) produces the duty ratios for the inverter which are sampling time dependent. However, in type-1 and type-2 fuzzy based space vector PWM algorithms, the three phases duty ratio

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19

Feng, Teng, Shuwei Deng, Xinglong Chen, Chao Zhang, and Yao Mao. "A Generalized Type-2 Fuzzy-Based Analog Memristive Controller." Electronics 14, no. 6 (2025): 1178. https://doi.org/10.3390/electronics14061178.

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Type-1 fuzzy PID controllers are widely used in industrial control systems due to their well-established theoretical foundation, simplicity of structure, and ease of operation. However, as control systems become increasingly complex and demands for higher control performance intensify, the limitations of Type-1 fuzzy controllers become more apparent. Additionally, the difficulty in tuning PID parameters and the inability to adjust these parameters online as the controlled system changes further constrain the effectiveness of traditional PID controllers. To address these challenges, this paper

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20

Wang, Li-Xin. "A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems." IEEE Transactions on Fuzzy Systems 25, no. 3 (2017): 693–706. http://dx.doi.org/10.1109/tfuzz.2016.2543746.

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Mendel, Jerry M., Robert I. John, and Feilong Liu. "Interval Type-2 Fuzzy Logic Systems Made Simple." IEEE Transactions on Fuzzy Systems 14, no. 6 (2006): 808–21. http://dx.doi.org/10.1109/tfuzz.2006.879986.

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Chen, Yang. "Study on Centroid Type-Reduction of Interval Type-2 Fuzzy Logic Systems Based on Noniterative Algorithms." Complexity 2019 (April 11, 2019): 1–12. http://dx.doi.org/10.1155/2019/7325053.

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Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many applications. While the block type-reduction under the guidance of inference plays the central role in the systems, Karnik-Mendel (KM) iterative algorithms are standard algorithms to perform the type-reduction; however, the high computational cost of type-reduction process may hinder them from real applications. The comparison between the KM algorithms and other alternative algorithms is still an open problem. This paper introduces the related theory of interval type-2 fuzzy sets and discusses the b

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23

Aminifar, Sadegh, and Arjuna Marzuki. "Uncertainty in Interval Type-2 Fuzzy Systems." Mathematical Problems in Engineering 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/452780.

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This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions) differentiate from T1MFs (type-1 membership functions) by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs). Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is int

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Cazarez-Castro, Nohe R., Luis T. Aguilar, and Oscar Castillo. "Designing Type-1 and Type-2 Fuzzy Logic Controllers via Fuzzy Lyapunov Synthesis for nonsmooth mechanical systems." Engineering Applications of Artificial Intelligence 25, no. 5 (2012): 971–79. http://dx.doi.org/10.1016/j.engappai.2012.03.003.

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Amador-Angulo, Leticia, Oscar Castillo, Patricia Melin, and Juan R. Castro. "Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous Mobile Robot." Micromachines 13, no. 9 (2022): 1490. http://dx.doi.org/10.3390/mi13091490.

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In this study, the first goal is achieving a hybrid approach composed by an Interval Type-3 Fuzzy Logic System (IT3FLS) for the dynamic adaptation of α and β parameters of Bee Colony Optimization (BCO) algorithm. The second goal is, based on BCO, to find the best partition of the membership functions (MFs) of a Fuzzy Controller (FC) for trajectory tracking in an Autonomous Mobile Robot (AMR). A comparative with different types of Fuzzy Systems, such as Fuzzy BCO with Type-1 Fuzzy Logic System (FBCO-T1FLS), Fuzzy BCO with Interval Type-2 Fuzzy Logic System (FBCO-IT2FLS) and Fuzzy BCO with Gener

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26

Niewiadomski, Adam, and Marcin Kacprowicz. "Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention." Journal of Artificial Intelligence and Soft Computing Research 11, no. 2 (2021): 85–97. http://dx.doi.org/10.2478/jaiscr-2021-0006.

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Abstract The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. T

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Wang, Dazhi, and Yang Chen. "Study on permanent magnetic drive forecasting by designing Takagi Sugeno Kang type interval type-2 fuzzy logic systems." Transactions of the Institute of Measurement and Control 40, no. 6 (2018): 2011–23. http://dx.doi.org/10.1177/0142331217694682.

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The process of permanent magnetic drive (PMD) presents high uncertainty under the complex operating conditions. In this paper, a type of Takagi Sugeno Kang (TSK) interval type-2 fuzzy logic systems (IT2 FLSs) under the Karnik-Mendel (KM) structure is designed for data-based PMD torque and revolutions per minute (rpm) forecasting. For designing the antecedent and input measurement of TSK IT2 FLSs, the primary membership functions (MFs) of interval type-2 fuzzy sets (IT2 FSs) are all selected as Gaussian type-2 MFs with uncertain derivation, while the consequent parameters are chosen as type-1 f

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28

Yang, Xiaozhan, Hak-Keung Lam, and Ligang Wu. "Membership-dependent stability conditions for type-1 and interval type-2 T–S fuzzy systems." Fuzzy Sets and Systems 356 (February 2019): 44–62. http://dx.doi.org/10.1016/j.fss.2018.01.018.

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Eyoh, I. J., U. A. Umoh, U. G. Inyang, and O. S. Adeoye. "Elliptic interval Type-2 intuitionistic fuzzy logic system for non-linear system identification." World Journal of Applied Science & Technology 15, no. 1 (2023): 48–54. http://dx.doi.org/10.4314/wojast.v15i1.48.

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An elliptic membership function has been proposed in the literature for interval type-2 fuzzy logic system. In this paper, elliptic non- membership function is incorporated into the conventional elliptic membership function model to obtain elliptic interval type-2 intuitionistic fuzzy sets for the first time. The elliptic interval type-2 intuitionistic fuzzy logic system so formed is applied for the prediction of two benchmark non-linear systems and results compared with Gaussian interval type-2 intuitionistic fuzzy logic system. Experimental results show that the elliptic interval type-2 intu

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Kahouli, O., B. Ashammari, K. Sebaa, M. Djebali, and H. H. Abdallah. "Type-2 Fuzzy Logic Controller Based PSS for Large Scale Power Systems Stability." Engineering, Technology & Applied Science Research 8, no. 5 (2018): 3380–86. http://dx.doi.org/10.48084/etasr.2234.

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In this paper, the application of the fuzzy logic based power systems stabilizer (FLPSS) to damp power system oscillation is presented. Various types of fuzzy logic controller are used to replace the conventional power system stabilizer (CPSS). The classic fuzzy logic controller based PSS (FLCPSS), the polar FLC (PFLCPSS) and the interval type-2 fuzzy logic controller based PSS (IT2FLCPSS) are applied to the New England - New York interconnected power system and the obtained results are compared. For coordination purposes, genetic algorithm (GA) is used to tune the FLCPSS’s gains. The non-line

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Kahouli, O., B. Alshammari, K. Sebaa, M. Jebali, and Abdallah H. Hadj. "Type-2 Fuzzy Logic Controller Based PSS for Large Scale Power Systems Stability." Engineering, Technology & Applied Science Research 8, no. 5 (2018): 3380–86. https://doi.org/10.5281/zenodo.1490364.

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In this paper, the application of the fuzzy logic based power systems stabilizer (FLPSS) to damp power system oscillation is presented. Various types of fuzzy logic controller are used to replace the conventional power system stabilizer (CPSS). The classic fuzzy logic controller based PSS (FLCPSS), the polar FLC (PFLCPSS) and the interval type-2 fuzzy logic controller based PSS (IT2FLCPSS) are applied to the New England - New York interconnected power system and the obtained results are compared. For coordination purposes, genetic algorithm (GA) is used to tune the FLCPSS’s gains. The no

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Biglarbegian, Mohammad, William Melek, and Jerry Mendel. "On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling." Information Sciences 181, no. 7 (2011): 1325–47. http://dx.doi.org/10.1016/j.ins.2010.11.003.

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Bernal, Emer, Oscar Castillo, José Soria, and Fevrier Valdez. "Generalized type-2 fuzzy logic in galactic swarm optimization: design of an optimal ball and beam fuzzy controller." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 3545–59. http://dx.doi.org/10.3233/jifs-191873.

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In this paper we present a modification based on generalized type-2 fuzzy logic to an algorithm that is inspired on the movement of large masses of stars and their attractive force in the universe, known as galactic swarm optimization (GSO). The modification consists on the dynamic adjustment of parameters in GSO using type-1 and type-2 fuzzy logic. The main idea of the proposed approach is the application of fuzzy systems to dynamically adapt the parameters of the GSO algorithm, which is then applied to parameter optimization of the membership functions of the bar and ball fuzzy controller. T

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Chen, Yang, and Jiaxiu Yang. "Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification." Transactions of the Institute of Measurement and Control 43, no. 12 (2021): 2780–87. http://dx.doi.org/10.1177/01423312211006635.

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In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with

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Guerrero, Maribel, Fevrier Valdez, and Oscar Castillo. "Comparative Study between Type-1 and Interval Type-2 Fuzzy Systems in Parameter Adaptation for the Cuckoo Search Algorithm." Symmetry 14, no. 11 (2022): 2289. http://dx.doi.org/10.3390/sym14112289.

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The objective of this work is focused on improving the optimization ability of the cuckoo search algorithm (CS), and, for this reason, a comparison is made between type-1 and interval type-2 fuzzy logic to look for more promising results in the cuckoo search algorithm (CS), and to help performance, we dynamically adjust the alpha parameter. The idea is to enable CS in leaving the local optima, and then be able to reach the global optima. Currently, there are good results in improving the optimization of algorithms through intelligent fuzzy logic computing after finding the best adjustment para

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Mendel, Jerry M., and Dongrui Wu. "Critique of “A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems”." IEEE Transactions on Fuzzy Systems 25, no. 3 (2017): 725–27. http://dx.doi.org/10.1109/tfuzz.2017.2648882.

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Qilian Liang and J. M. Mendel. "Interval type-2 fuzzy logic systems: theory and design." IEEE Transactions on Fuzzy Systems 8, no. 5 (2000): 535–50. http://dx.doi.org/10.1109/91.873577.

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Mendel, J. M. "Computing Derivatives in Interval Type-2 Fuzzy Logic Systems." IEEE Transactions on Fuzzy Systems 12, no. 1 (2004): 84–98. http://dx.doi.org/10.1109/tfuzz.2003.822681.

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Niewiadomski, A., and M. Kacprowicz. "Higher order fuzzy logic in controlling selective catalytic reduction systems." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 4 (2014): 743–50. http://dx.doi.org/10.2478/bpasts-2014-0080.

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Abstract This paper presents research on applications of fuzzy logic and higher-order fuzzy logic systems to control filters reducing air pollution [1]. The filters use Selective Catalytic Reduction (SCR) method and, as for now, this process is controlled manually by a human expert. The goal of the research is to control an SCR system responsible for emission of nitrogen oxide (NO) and nitrogen dioxide (NO2) to the air, using SCR with ammonia (NH3). There are two higher-order fuzzy logic systems presented, applying interval-valued fuzzy sets and type-2 fuzzy sets, respectively. Fuzzy sets and

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Khosravi, Abbas, and Saeid Nahavandi. "Load Forecasting Using Interval Type-2 Fuzzy Logic Systems: Optimal Type Reduction." IEEE Transactions on Industrial Informatics 10, no. 2 (2014): 1055–63. http://dx.doi.org/10.1109/tii.2013.2285650.

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Michal, Blaho, Murgaš J´n, Viszus Eugen, and Fodrek Peter. "Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic." Journal of Electrical Engineering 65, no. 6 (2015): 364–70. http://dx.doi.org/10.2478/jee-2014-0059.

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Abstract The networked control systems (NCS) have grown in popularity in recent years. Despite their advantages over the traditional control schemes, some of their drawbacks emerged as well (time delays, packet losses). There are several ways of dealing with the time delays and packet losses in NCS, but only a few authors have ever used type-2 fuzzy controllers for this purpose to our knowledge. This paper is aimed at dealing with the negative effects that occur in NCS, by using type-2 fuzzy control systems. It is presented that this approach can be successfully used to decrease the effects of

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Castillo, Oscar, Juan R. Castro, Patricia Melin, and Antonio Rodriguez-Diaz. "Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks in Nonlinear Identification." Advances in Fuzzy Systems 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/136214.

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Neural networks (NNs), type-1 fuzzy logic systems (T1FLSs), and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be universal approximators, which means that they can approximate any nonlinear continuous function. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling imperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy neural network (IT2FNN) is a universal approximator, which uses a set of rules and interv

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Miron, Anca, Andrei C. Cziker, and Horia G. Beleiu. "Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency." Applied Sciences 14, no. 11 (2024): 4468. http://dx.doi.org/10.3390/app14114468.

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Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal data

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Cara, Ana Belen, Christian Wagner, Hani Hagras, Hector Pomares, and Ignacio Rojas. "Multiobjective Optimization and Comparison of Nonsingleton Type-1 and Singleton Interval Type-2 Fuzzy Logic Systems." IEEE Transactions on Fuzzy Systems 21, no. 3 (2013): 459–76. http://dx.doi.org/10.1109/tfuzz.2012.2236096.

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Sun, Zhe, Ning Wang, and Yunrui Bi. "Type-1/type-2 fuzzy logic systems optimization with RNA genetic algorithm for double inverted pendulum." Applied Mathematical Modelling 39, no. 1 (2015): 70–85. http://dx.doi.org/10.1016/j.apm.2014.04.035.

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Ochoa, Patricia, Cinthia Peraza, Oscar Castillo, and Zong Woo Geem. "A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution." Algorithms 16, no. 6 (2023): 279. http://dx.doi.org/10.3390/a16060279.

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The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed t

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Francisco Trejo, Rafael Torres Escobar, and Alberto Ochoa-Zezzatti. "Forecasting 3PL demand of warehousing services with interval type-3 fuzzy logic and GM (1,1)." International Journal of Combinatorial Optimization Problems and Informatics 15, no. 4 (2024): 186–98. http://dx.doi.org/10.61467/2007.1558.2024.v15i4.441.

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Real-world information can be vague or imprecise, not reliable, where the information is presented in fragments, ambiguity in the data, or even contradictory information, these can lead to uncertainty, but even this uncertainty we need to take decisions [1]. Part of this uncertainty can be handled by different un-certainty models, such: grey systems [2-3], type-1, type-2 [4-7] or typ-3 fuzzy systems, all used represent this uncertainty with numbers. But some-times, there more complex situations are, it is extremely difficult to find the precise numeric value or model to provide accurate value

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Ahamad, Faras. "COMPARISON OF CONVENTIONAL -TYPE AND FUZZY-TYPE PID CONTROLLER." International Journal of Advances in Engineering & Scientific Research 1, no. 8 (2014): 01–06. https://doi.org/10.5281/zenodo.10725346.

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<strong>Abstract:  </strong> <em>The best known controllers used in the industries are the proportional - integral - derivative (PID) controller because of their simple structure and robust performance in wide operating conditions. But the performance of these controller get affected with parameters variation and with the increase in the complexity of the system    After being mostly viewed as a controversial technology for two decades, fuzzy logic has finally been accepted as an emerging technology. This is largely due to a wide array of successful applications ranging fro

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Loukal, Keltoum, and Leila Benalia. "Type-2 Fuzzy Logic Controller of a Doubly Fed Induction Machine." Advances in Fuzzy Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8273019.

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Interval type-2 fuzzy logic controller (IT2FLC) method for controlling the speed with a direct stator flux orientation control of doubly fed induction motor (DFIM) is proposed. The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases it is proved that their robustness and performance are less sensitive to parameters variation over conventional controllers. The synthesis of stabilizing control laws design based on IT2FLC is developed. A comparative analysis between type-1 fuzzy logic controller (T1FLC) and IT2FLC of the DFIM is shown. Si

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Khemis, A., T. Boutabba, and S. Drid. "Model reference adaptive system speed estimator based on type-1 and type-2 fuzzy logic sensorless control of electrical vehicle with electrical differential." Electrical Engineering & Electromechanics, no. 4 (June 27, 2023): 19–25. http://dx.doi.org/10.20998/2074-272x.2023.4.03.

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Introduction. In this paper, a new approach for estimating the speed of in-wheel electric vehicles with two independent rear drives is presented. Currently, the variable-speed induction motor replaces the DC motor drive in a wide range of applications, including electric vehicles where quick dynamic response is required. This is now possible as a result of significant improvements in the dynamic performance of electrical drives brought about by technological advancements and development in the fields of power commutation devices, digital signal processing, and, more recently, intelligent contr

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