Relevant bibliographies by topics / Type-1 fuzzy logic

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Type-1 fuzzy logic'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Type-1 fuzzy logic"

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Lalka, Neeru, and Sushma Jain. "Comparative Study of Type-1 Fuzzy Logic and Type-2 Fuzzy Logic." International Journal of Computer Applications 124, no. 16 (2015): 14–21. http://dx.doi.org/10.5120/ijca2015905802.

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Handayani, Ade Silvia, Nyayu Latifah Husni, Siti Nurmaini, and Irsyadi Yani. "Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real Swarm Robot." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 06 (2019): 83. http://dx.doi.org/10.3991/ijoe.v15i06.10075.

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Navigation is one of the typical problem domains occurred in studying swarm robot. This task needs a special ability in avoiding obstacles. This research presents the navigation techniques using type 1 fuzzy logic and interval type 2 fuzzy logic. A comparison of those two fuzzy logic performances in controlling swarm robot as tools for complex problem modeling, especially for path navigation is presented in this paper. Each hierarchical of fuzzy logic shows its advantages and disadvantages. For testing the robustness of type-1 fuzzy logic and interval type-2 fuzzy logic algorithms, 3 robots fo
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Maity, Saikat, Sanjay Chakraborty, Saroj Kumar Pandey, Indrajit De, and Sourasish Nath. "Type-n fuzzy logic - the next level of type-1 and type-2 fuzzy logic." International Journal of Intelligent Engineering Informatics 11, no. 4 (2023): 353–89. http://dx.doi.org/10.1504/ijiei.2023.136106.

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Castillo, Oscar, and Patricia Melin. "Proposal for Mediative Fuzzy Control: From Type-1 to Type-3." Symmetry 15, no. 10 (2023): 1941. http://dx.doi.org/10.3390/sym15101941.

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This paper presents an initial proposal for the utilization of mediative fuzzy logic in control problems. Mediative fuzzy logic (MFL) was originally proposed with the idea of modeling situations in which there exists contradictory knowledge among several experts in an application domain. In this situation, a mediative solution may be a better choice in this particular decision-making situation. In this paper, we are extending the concept of fuzzy control to the realm of MFL for situations in which we have two or more control experts, and the design of the fuzzy controller has to be based on th
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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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Dominik, Ireneusz. "Implementation of the Type-2 Fuzzy Controller in PLC." Solid State Phenomena 164 (June 2010): 95–98. http://dx.doi.org/10.4028/www.scientific.net/ssp.164.95.

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The main aim of the presented research work was to develop type-2 fuzzy logic controller, which by its own design should be “more intelligent” than type-1. Along with the intelligence it should provide better results in solving a particular problem. Type-2 fuzzy logic controller is not well-known and it is rarely used at present. The idea of type-2 fuzzy logic set was presented by Zadeh in 1975, shortly after the presentation of type-1 fuzzy set. At the beginning scientists and researchers worked on type-1. Only after developing type-1 the attention was directed towards the type-2. The first a
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Arifin, Bustanul, Bhakti Yudho Suprapto, Sri Arttini Dwi Prasetyowati, and Zainuddin Nawawi. "Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control." World Electric Vehicle Journal 13, no. 3 (2022): 53. http://dx.doi.org/10.3390/wevj13030053.

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The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the st
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Navita Dhaka. "A Comparative Study of Fuzzy Logic Type-1, Type-2 and Gradient-Based Method for Edge Detection in RGB Images." Communications on Applied Nonlinear Analysis 31, no. 7s (2024): 170–76. http://dx.doi.org/10.52783/cana.v31.1292.

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Edge detection is widely used in image processing to simplify the images by reducing data. It emphasizes essential features and thereby enhances data processing speed. Edge detection techniques are widely used in the fields of Medical Imaging, Vehicle Detection, Fingerprint Recognition, Robotic vision, Steganography, Feature extraction, security etc. This research paper presents a comparative analysis of different edge detection techniques based on gradient fuzzy Type-1 and fuzzy Type-2. The researcher has experimentally evaluated the edges detected using Prewitt operator based on Gradients, m
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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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Janková, Zuzana, and Petr Dostál. "Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty." Mathematical Problems in Engineering 2021 (June 18, 2021): 1–16. http://dx.doi.org/10.1155/2021/3839071.

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Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the
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Dissertations / Theses on the topic "Type-1 fuzzy logic"

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Ehtiawesh, Mohamed. "Investigations into new algorithms for self-organising fuzzy logic control using type-1 and type-2 fuzzy sets." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/17997/.

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The number of applications of intelligent control systems has grown significantly over the last few decades, and today they are used in various challenging industrial application domains, where they provide particularly useful solutions. The term „intelligent controllers‟ describes a field where control approaches are represented by mechanisms similar to those used by the human brain. These characteristics include, for example, learning, modification, adaption, effective working with high levels of uncertainty and coping with large amounts of data. Intelligent control systems are particularly
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Amaral, Renan Piazzaroli Finotti. "Type-1 and singleton fuzzy logic system trained by a fast scaled conjugate gradient methods for dealing with classification problems." Universidade Federal de Juiz de Fora (UFJF), 2017. https://repositorio.ufjf.br/jspui/handle/ufjf/6034.

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Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-01-09T13:48:15Z No. of bitstreams: 1 renanpiazzarolifinottiamaral.pdf: 1172046 bytes, checksum: eb7bf10c813d64fbddcc572d39aecfc5 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-01-22T16:10:30Z (GMT) No. of bitstreams: 1 renanpiazzarolifinottiamaral.pdf: 1172046 bytes, checksum: eb7bf10c813d64fbddcc572d39aecfc5 (MD5)<br>Made available in DSpace on 2018-01-22T16:10:30Z (GMT). No. of bitstreams: 1 renanpiazzarolifinottiamaral.pdf: 1172046 bytes, checksum: eb7bf10c813d64fbddcc572d39aecfc
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Chen, Carla Chia-Ming. "Bayesian methodology for genetics of complex diseases." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf.

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Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interacti
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Chan-HongChao and 趙全鋐. "Design and Application of Type-1 Fuzzy Logic Controller and Interval Type-2 Fuzzy Immune Controller." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/55092220871610339589.

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博士<br>國立成功大學<br>電機工程學系碩博士班<br>98<br>This dissertation focuses on the applications of type-1 fuzzy controller and the development of a new kind of fuzzy controller, the interval type-2 fuzzy immune controller. Firstly, an omni-directional vision-based control scheme for the car-like mobile robot (CLMR) is presented. From the image information, one can estimate the position of the CLMR in the parking space and figure out a feasible reference path. Then, we propose a fuzzy logical control to manipulate the steering wheel such that it can execute parallel-parking missions. On the other hand, an im
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Padhi, Ashutosh. "Hardware Realization of Interval Type 2 Fuzzy Logic Controller." Thesis, 2015. http://ethesis.nitrkl.ac.in/7740/1/603.pdf.

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Form the publication of Prof. Loftis’ fuzzy set theory and linguistic approach, fuzzy logic has become one of the major research topic for researchers. The type 1 fuzzy logic controller has already been used in many areas such as consumer electronics, defense, aerospace engineering etc. This type 1 fuzzy system does not model the fuzziness of the membership function or in other terms it does not deal with uncertainty very well. The control problems where more accuracy is needed, type 1 fuzzy system unable to satisfy their requirements. If a type 2 fuzzy controller is designed, it will have gre
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Chen, Yu-Jhong, and 陳昱中. "Multiobjective Evolutionary Interpretable Type 1 and Type 2 Fuzzy Controller Optimization With Robot Control Applications." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5441081%22.&searchmode=basic.

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碩士<br>國立中興大學<br>電機工程學系所<br>107<br>This thesis presents two main issues in multiobjective evolutionary interpretable fuzzy systems (FSs). The first issue is about the specification of merging and interpretability of fuzzy sets. In general, the fuzzy sets of an FS through optimization have high overlap and excursive distribution. By using the merging technique to process the fuzzy sets with high overlap, an appropriate overlapping degree and evenly distributed fuzzy sets would be obtained. The interpretability is to verify the distribution of the merged fuzzy sets and give corresponding assessme
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Prusty, Debajyoti. "Implementation of Pi, Pd, And Pi+D Type Fuzzy Logic Controller of SISO and MIMO Level Process and Their Performance Analysis." Thesis, 2016. http://ethesis.nitrkl.ac.in/9208/1/2016_MT_DPrusty.pdf.

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This thesis presents the implementation of fuzzy logic controllers (FLCs) and scrutinizes the performance of FLC controllers with IMC and conventional PID controller. The core of the FLC design is based on Mamdani model. Various controllers such as Proportional Integral (PI) FLC, Proportional-Derivative (PD) FLC, and PI+D FLC (PI fuzzy plus conventional derivative controller) are designed. Different Level Process such As SISO Three Tank Systems (one manipulated variable and one controlled variable), MIMO Two Tank Systems (two manipulated variables and two controlled variables) and MIMO Three T
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Books on the topic "Type-1 fuzzy logic"

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Ponce-Cruz, Pedro, Arturo Molina, and Brian MacCleery. Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26656-5.

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Molina, Arturo, Pedro Ponce-Cruz, and Brian MacCleery. Fuzzy Logic Type 1 and Type 2 Based on LabVIEW(tm) FPGA. Springer, 2015.

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Molina, Arturo, Pedro Ponce-Cruz, and Brian MacCleery. Fuzzy Logic Type 1 and Type 2 Based on LabVIEW(tm) FPGA. Springer, 2016.

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Molina, Arturo, Pedro Ponce-Cruz, and Brian MacCleery. Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA. Springer, 2018.

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Book chapters on the topic "Type-1 fuzzy logic"

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Castillo, Oscar, and Patricia Melin. "Type-1 Fuzzy Logic." In Soft Computing and Fractal Theory for Intelligent Manufacturing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1766-9_2.

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Melin, Patricia, and Oscar Castillo. "Type-1 Fuzzy Logic." In Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32378-5_2.

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Starczewski, Janusz T. "A Type-1 Approximation of Interval Type-2 FLS." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02282-1_36.

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Castillo, Oscar, and Patricia Melin. "2 Type-1 Fuzzy Logic." In Type-2 Fuzzy Logic: Theory and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76284-3_2.

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Mendel, Jerry M. "Type-1 Fuzzy Sets and Fuzzy Logic." In Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51370-6_2.

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Mendel, Jerry M. "Type-1 Fuzzy Sets and Fuzzy Logic." In Explainable Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-35378-9_2.

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Ponce-Cruz, Pedro, Arturo Molina, and Brian MacCleery. "Real-Time Fuzzy Logic Controllers." In Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26656-5_3.

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Fazel Zarandi, M. H., R. Gamasaee, and O. Castillo. "Type-1 to Type-n Fuzzy Logic and Systems." In Fuzzy Logic in Its 50th Year. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31093-0_6.

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Castillo, Oscar, and Patricia Melin. "1 Introduction to Type-2 Fuzzy Logic." In Type-2 Fuzzy Logic: Theory and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76284-3_1.

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Castillo, Oscar, and Luis T. Aguilar. "Background on Type-1 and Type-2 Fuzzy Logic." In Type-2 Fuzzy Logic in Control of Nonsmooth Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03134-3_1.

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Conference papers on the topic "Type-1 fuzzy logic"

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Liu, Xuan, Feng Hu, Zuqiang Su, and Fusong Yang. "Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logic and Reinforcement Learning." In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024. https://doi.org/10.1109/aiim64537.2024.10934697.

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Suman, Saurabh, Utkarsha Pacharaney, and Pradnyawant M. Gote. "A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967700.

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Aladi, Jabran Hussain, Christian Wagner, Amir Pourabdollah, and Jonathan M. Garibaldi. "Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737943.

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Aladi, Jabran Hussain, Christian Wagner, Jonathan M. Garibaldi, and Amir Pourabdollah. "On transitioning from type-1 to interval type-2 fuzzy logic systems." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338004.

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Wu, Dongrui, and Jerry M. Mendel. "Examining the continuity of type-1 and interval type-2 fuzzy logic systems." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584482.

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Mendel, Jerry M. "A quantitative comparison of interval type-2 and type-1 fuzzy logic systems: First results." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584727.

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Naik, K. A., and C. P. Gupta. "Performance comparison of Type-1 and Type-2 fuzzy logic systems." In 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2017. http://dx.doi.org/10.1109/ispcc.2017.8269652.

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Dongming Wang and L. Acar. "An analysis of type-1 and type-2 fuzzy logic systems." In Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014). IEEE, 1999. http://dx.doi.org/10.1109/isic.1999.796680.

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Chiclana, Francisco, and Shang-Ming Zhou. "The Type-1 OWA Operator and the Centroid of Type-2 Fuzzy Sets." In 7th conference of the European Society for Fuzzy Logic and Technology. Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.138.

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Cherroun, L., M. Nadour, and A. Kouzou. "Type-1 and Type-2 Fuzzy Logic Controllers for Autonomous Robotic Motion." In 2019 International Conference on Applied Automation and Industrial Diagnostics (ICAAID). IEEE, 2019. http://dx.doi.org/10.1109/icaaid.2019.8934997.

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