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Relevant bibliographies by topics / Mamdani and Sugeno inference systems

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Academic literature on the topic 'Mamdani and Sugeno inference systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Mamdani and Sugeno inference systems"

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Subramaniam, Prabhakar Rontala, and Chitra Venugopal. "Comparison of Mamdani and Sugeno Inference Methods in Calorie Burn Calculation for Activity Using Treadmill." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1703–9. http://dx.doi.org/10.1166/jctn.2020.8428.

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This research provides a comparison between the performance of Mamdani and Sugeno Fuzzy Inference System in Calorie Burn calculation for treadmill workout exercise. The results are compared against the calculation calorie burn value using traditional formula. The objective of this results to get a system which provides calorie burn results closer to the traditionally calculated value. The Mamdani and Sugeno systems are designed with two input membership functions namely Incline and Speed and one output membership function called Calorie Burn as used in the traditional calculation method. The fuzzy rule table is designed using 7 Membership function for Incline and speed inputs and 9 Membership function for calorie burn output. The Mamdani and Sugeno system are tested with the same fuzzy rules for better comparison. The results are tabulated with theoretical values compared with Mamdani and Sugeno system for 5 incline levels and 7 speed levels. It can be observed that the Sugeno system results are closer to the traditional calculation results. This is shows that the sugeno system works better than Mamdani system for linear system. Also it depicts the calculated values and hence it can be used as a replacement for tedious mathematical analysis.

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Mudia, Halim. "Comparative Study of Mamdani-type and Sugeno-type Fuzzy Inference Systems for Coupled Water Tank." Indonesian Journal of Artificial Intelligence and Data Mining 3, no. 1 (2020): 42. http://dx.doi.org/10.24014/ijaidm.v3i1.9309.

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The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Threfore, in this paper will use fuzzy inference systems to control of level 2 are developed using Mamdani-type and Sugeno-type fuzzy models. The outcome obtained by two fuzzy inference systems is evaluated. This paper summarizes the essential variation among the Mamdani-type and Sugeno-type fuzzy inference systems with setpoint of level is 10 centimeter. Matlab fuzzy logic toolbox is used for the simulation of both the models. This also confirms which one is a superior choice of the two fuzzy inference systems to control of level 2 in tank 2. The results show madani-type fuzzy inference system is superior as compared to sugeno-type fuzzy inference system.

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Sarimuthu, Charles R., Vigna K. Ramachandaramurthy, H. Mokhlis, and K. R. Agileswari. "Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Transformer Tap Changing System." International Journal of Advances in Applied Sciences 5, no. 4 (2016): 163. http://dx.doi.org/10.11591/ijaas.v5.i4.pp163-167.

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<p>Voltage control is considered one of the basic operational requirements of electrical power systems. The most popular voltage control equipment includes On-Load Tap Changer (OLTC) transformer controlled by Automatic Voltage Control (AVC) relay. Recent studies have shown that fuzzy inference systems (FIS) are applicable for transformer tap changing system. In this paper, FIS are developed for transformer tap changing system using Mamdani-type and Sugeno-type fuzzy models. The results of the two fuzzy inference systems (FIS) are compared. The basic difference between the Mamdani-type FIS and Sugeno-type FIS is included in this paper. It also shows which one is a more suitable choice of the two FIS for transformer tap changing system.</p>

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Supatmi, Sri, Rongtao Hou, and Irfan Dwiguna Sumitra. "Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia." Computational Intelligence and Neuroscience 2019 (February 25, 2019): 1–13. http://dx.doi.org/10.1155/2019/6203510.

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An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.

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Carneiro, Vinícius Quintão, Adalgisa Leles do Prado, Cosme Damião Cruz, Pedro Crescêncio Souza Carneiro, Moysés Nascimento, and José Eustáquio De Souza Carneiro. "Fuzzy control systems for decision-making in cultivars recommendation." Acta Scientiarum. Agronomy 40, no. 1 (2018): 39314. http://dx.doi.org/10.4025/actasciagron.v40i1.39314.

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The objective of the present study was to propose fuzzy control systems to support the recommendation of cultivars of different agronomic crops. Grain yield data from 23 lines and 2 cultivars of red bean were used to evaluate the applicability of these controllers. Genotypes were evaluated in nine environments in the Zona da Mata region, Minas Gerais State, Brazil. Using the parameters of Eberhart and Russell analysis, fuzzy controllers were developed with the Mamdani and Sugeno inference systems. Analyses of adaptability and stability were carried out by the method of Eberhart and Russell. The parameters obtained for each genotype were submitted to the respective controllers. There were significant genotypes x environments interaction, which justified the necessity of performing an adaptability and stability analysis. For both controllers (Mamdani and Sugeno), seven lines presented general adaptability, while only one presented adaptability to unfavorable environments. It was also found that both inference systems were useful for developing controllers that had the aim of recommending cultivars. Thus, it was noted that fuzzy control systems have the potential to identify the behavior of bean genotypes.

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Jacquin, Alexandra P., and Asaad Y. Shamseldin. "Review of the application of fuzzy inference systems in river flow forecasting." Journal of Hydroinformatics 11, no. 3-4 (2009): 202–10. http://dx.doi.org/10.2166/hydro.2009.038.

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This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi–Sugeno–Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.

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Kumar, Neerendra, and Zoltán Vámossy. "Robot navigation with obstacle avoidance in unknown environment." International Journal of Engineering & Technology 7, no. 4 (2018): 2410. http://dx.doi.org/10.14419/ijet.v7i4.14767.

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In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

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Vijayakumar, S., and V. Santhi. "Speckle Noise Reduction in SAR Images Using Fuzzy Inference System." International Journal of Fuzzy System Applications 8, no. 4 (2019): 60–83. http://dx.doi.org/10.4018/ijfsa.2019100104.

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In recent years, image processing has played a vital role in major research areas. In this article, a new approach using a fuzzy inference system is proposed for speckle reduction in SAR images. In general, SAR images are predominantly used to monitor coastal regions to detect oil spills, ship wake, sea shores and climate changes. In this article, a gamma distribution model is used in a fuzzy inference system to remove speckle noise from SAR images. The performance of the proposed model is tested using fuzzy inference systems, such as mamdani and sugeno. The experimental results proved the efficiency of the proposed system through objective metrics.

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Khosravanian, Rassoul, Mohammad Sabah, David A. Wood, and Ahmad Shahryari. "Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared." Journal of Natural Gas Science and Engineering 36 (November 2016): 280–97. http://dx.doi.org/10.1016/j.jngse.2016.10.046.

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Subhedar, Mansi, and Gajanan Birajdar. "Comparison of Mamdani and Sugeno Inference Systems for Dynamic Spectrum Allocation in Cognitive Radio Networks." Wireless Personal Communications 71, no. 2 (2012): 805–19. http://dx.doi.org/10.1007/s11277-012-0845-6.

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Dissertations / Theses on the topic "Mamdani and Sugeno inference systems"

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GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.

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Book chapters on the topic "Mamdani and Sugeno inference systems"

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Chaudhary, Alka. "Mamdani and Sugeno Fuzzy Inference Systems’ Comparison for Detection of Packet Dropping Attack in Mobile Ad Hoc Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1501-5_70.

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Yaguinuma, Cristiane A., Walter C. P. Magalhães, Marilde T. P. Santos, Heloisa A. Camargo, and Marek Reformat. "Combining Fuzzy Ontology Reasoning and Mamdani Fuzzy Inference System with HyFOM Reasoner." In Enterprise Information Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09492-2_11.

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Majumder, Debasish, Joy Debnath, and Animesh Biswas. "Interval Type-2 Mamdani Fuzzy Inference System for Morningness Assessment of Individuals." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3174-8_57.

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Sharma, Vedna, and Sourabh Jain. "Analysis of Faculty Teaching Performance Based on Student Feedback Using Fuzzy Mamdani Inference System." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4087-9_36.

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Majumder, Debasish, Mrinmoy Dam, Rupak Bhattacharjee, Shyam Sundar Santra, Rishiraj Saha, and Soham Saha. "Performance Measurement Model for Wind Power Project Using Mamdani Fuzzy Inference System." In Proceedings of the International Conference on Computing and Communication Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4084-8_24.

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Hájek, Petr, and Vladimír Olej. "Adaptive Intuitionistic Fuzzy Inference Systems of Takagi-Sugeno Type for Regression Problems." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33409-2_22.

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Melin, Patricia. "Image Processing and Pattern Recognition with Mamdani Interval Type-2 Fuzzy Inference Systems." In Combining Experimentation and Theory. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24666-1_13.

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Sakharova, Lyudmila V., Sergey V. Rogozhin, and Alexander N. Kuzminov. "Aggregation of Enterprise Bankruptcy Risk Assessments Based on Logit Complex—Mamdani Models and Fuzzy Logic Inference." In Complex Systems: Innovation and Sustainability in the Digital Age. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44703-8_13.

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Kumari, Nandini, Shamama Anwar, and Vandana Bhattacharjee. "A Takagi-Sugeno Based Fuzzy Inference System for Predicting Electrical Output in a Combined Cycle Power Plant." In Nanoelectronics, Circuits and Communication Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2854-5_8.

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Ontiveros-Robles, Emanuel, Patricia Melin, and Oscar Castillo. "Comparative Analysis of Type-1 Fuzzy Inference Systems with Different Sugeno Polynomial Orders Applied to Diagnosis Problems." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21920-8_41.

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Conference papers on the topic "Mamdani and Sugeno inference systems"

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Gaur, Vibha, Anuja Soni, S. K. Muttoo, and Neeraj Jain. "Comparative analysis of Mamdani and Sugeno inference systems for evaluating inter-agent dependency requirements." In 2012 12th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2012. http://dx.doi.org/10.1109/his.2012.6421322.

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Ho, Weng Luen, Whye Loon Tung, and Chai Quek. "An evolving Mamdani-Takagi-Sugeno based neural-fuzzy inference system with improved interpretability-accuracy." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584831.

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Wang, Yang, and Yanyan Chen. "A comparison of Mamdani and Sugeno fuzzy inference systems for chaotic time series prediction." In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6525972.

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Tung, W. L., and C. Quek. "A mamdani-takagi-sugeno based linguistic neural-fuzzy inference system for improved interpretability-accuracy representation." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277194.

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Singla, Jimmy. "Comparative study of Mamdani-type and Sugeno-type fuzzy inference systems for diagnosis of diabetes." In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, 2015. http://dx.doi.org/10.1109/icacea.2015.7164799.

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Topaloglu, Fatih, and Huseyin Pehlivan. "Comparison of Mamdani type and Sugeno type fuzzy inference systems in wind power plant installations." In 2018 6th International Symposium on Digital Forensic and Security (ISDFS). IEEE, 2018. http://dx.doi.org/10.1109/isdfs.2018.8355384.

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dos Reis Junior, Jose Valdemir, Thiago R. Raddo, Anderson L. Sanches, and Ben-Hur V. Borges. "Comparison between Mamdani and Sugeno fuzzy inference systems for the mitigation of environmental temperature variations in OCDMA-PONs." In 2015 17th International Conference on Transparent Optical Networks (ICTON). IEEE, 2015. http://dx.doi.org/10.1109/icton.2015.7193509.

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Hamam, Abdelwahab, and Nicolas D. Georganas. "A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications." In 2008 IEEE International Workshop on Haptic Audio visual Environments and Games (HAVE 2008). IEEE, 2008. http://dx.doi.org/10.1109/have.2008.4685304.

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Rane, Shubhechchha, L. K. Ragha, and Deepak Kurule. "Analysis of Mamdani and Sugeno fuzzy inference system for destroying multiple target at high angle of attack using Simulink." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8203592.

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Bacha, Salem B., and Barnabas Bede. "On Takagi Sugeno approximations of Mamdani fuzzy systems." In 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS). IEEE, 2016. http://dx.doi.org/10.1109/nafips.2016.7851602.

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