Relevant bibliographies by topics / Fuzzy logic – Industrial applications – Congresses

Academic literature on the topic 'Fuzzy logic – Industrial applications – Congresses'

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Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Fuzzy logic – Industrial applications – Congresses"

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Barrett, J. Douglas. "Advanced Fuzzy Logic Technologies in Industrial Applications." Technometrics 49, no. 4 (November 2007): 494–95. http://dx.doi.org/10.1198/tech.2007.s689.

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Bonissone, P. P., V. Badami, K. H. Chiang, P. S. Khedkar, K. W. Marcelle, and M. J. Schutten. "Industrial applications of fuzzy logic at General Electric." Proceedings of the IEEE 83, no. 3 (March 1995): 450–65. http://dx.doi.org/10.1109/5.364490.

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Meier, Willi, Richard Weber, and Hans-Jurgen Zimmermann. "Fuzzy data analysis — Methods and industrial applications." Fuzzy Sets and Systems 61, no. 1 (January 1994): 19–28. http://dx.doi.org/10.1016/0165-0114(94)90280-1.

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Turc, Cristian Gheorghe, and George Belgiu. "Fuzzy Logic Applications in Flanges Manufacturing." Advanced Materials Research 837 (November 2013): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.837.223.

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The industrial engineering experts consider the product early design stage as one of the most important stage of the design process, because it influences all stages of the product life cycle. One of the capital questions for manufacturing or design engineering people is how simple or elaborated work piece to use for a given part, in a specific set of production conditions. The optimization problem consists of the choosing of the right work piece for the current production conditions. In many cases this problem is solved empirically, based on the experience of the manufacturer. This approach leads to results that are situated more or less close to the optimum for the technical and economical point of view. Fuzzy logic is a method that is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. For the parts manufacturing, the main idea of the algorithm is to formalize the knowledge regarding production volume, geometry, loading conditions and other factors, using fuzzy sets and then to take the work piece choosing decision through the inference rules that are specified to fuzzy logic methodology. The described fuzzy logic algorithm allows a rapid, argued choosing of the work piece type in the production. The paper describes the method implementation for the manufacturing of flanges, including the description of the software results.
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Turc, Cristian Gheorghe, Felicia Banciu, and George Belgiu. "Fuzzy Logic Applications in Gears Manufacturing." Advanced Materials Research 1036 (October 2014): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.1028.

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The experts of industrial design field consider the product early design stage as one of the most important stage of the design process, because it influences all stages of the product life cycle. One of the capital questions for manufacturing or design engineering people is how simple or elaborated work piece to use for a given part, in a specific set of production conditions. The optimization problem consists of the choosing of the right work piece for the current production conditions. In many cases this problem is solved empirically, based on the experience of the manufacturer. This approach leads to results that are situated more or less close to the optimum for the technical and economical point of view. Fuzzy logic is a method that is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. For the parts manufacturing, the main idea of the algorithm is to formalize the knowledge regarding production volume, loading conditions and other factors, using fuzzy sets and then to take the work piece choosing decision through the inference rules that are specified to fuzzy logic methodology. The described fuzzy logic algorithm allows a rapid, argued choosing of the work piece type in the production. The paper describes the method implementation for the manufacturing of gears, including the description of the software results and their interpretation.
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Efstathiou, Janet. "Applications of fuzzy set methodologies in industrial engineering." Fuzzy Sets and Systems 36, no. 3 (July 1990): 405. http://dx.doi.org/10.1016/0165-0114(90)90116-n.

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Karwowski, Waldemar, and Anil Mital. "Potential applications of fuzzy sets in industrial safety engineering." Fuzzy Sets and Systems 19, no. 2 (June 1986): 105–20. http://dx.doi.org/10.1016/0165-0114(86)90031-x.

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Rana, Sudesh. "DEVELOPMENT OF PID LIKE FLC ALGORITHM FOR INDUSTRIAL APPLICATIONS." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 12–22. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.26.

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Now a day, in many industries different types of controllers (PD, PID, PLC, FLC etc.) are used. One of them is fuzzy logic controller. Here we develop a PID like fuzzy logic controller for industrial application, such application is water purification plant. For developing the PID like FLC, first we have to design a PID algorithm than we develop an algorithm for fuzzy logic controller. By comparing this two of controller we will develop a PID like FLC. A simple PID controller is sum of three type of controller proportional, integral and derivative controller, after simulated on MATLAB. Same cases we can be develop a structure of FLC for water purification plant. In the water purification plant raw water or ground water is promptly purified by injecting chemical rates at rates, related to water quality [13][2]. The feed of chemical rate judged and determined by the skilled operator. Here we try to develop an FLC algorithm so that the feed rate of coagulant is can be judged automatically without any skilled operator, than compose a PID like FLC for water purification plant process.
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Katayama, Ryu. "Applications of Neuro Fuzzy Technology in Consumer Electronics Products." Journal of Robotics and Mechatronics 7, no. 1 (February 20, 1995): 2–8. http://dx.doi.org/10.20965/jrm.1995.p0002.

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In recent years, intelligent industrial systems and consumer electronic products have been widely and intensively developed. Fuzzy logic, neural network, and neuro fuzzy technology, which integrates both approaches, are now regarded as an effective method to realize such intelligent features. In this paper, a review of the fuzzy boom in the consumer electronics market of Japan is presented. Typical applications of home appliances using fuzzy logic and neuro fuzzy technology are then described. Finally, methods and tools for developing fuzzy systems such as self-tuning and fuzzy modeling are reviewed.
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Sultan. "Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic." Journal of Computer Science 7, no. 10 (October 1, 2011): 1574–80. http://dx.doi.org/10.3844/jcssp.2011.1574.1580.

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Dissertations / Theses on the topic "Fuzzy logic – Industrial applications – Congresses"

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-, Tarunamulia Biological Earth &amp Environmental Sciences Faculty of Science UNSW. "Application of fuzzy logic, GIS and remote sensing to the assessment of environmental factors for extensive brackishwater aquaculture in Indonesia." Publisher:University of New South Wales. Biological, Earth & Environmental Sciences, 2008. http://handle.unsw.edu.au/1959.4/41444.

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Extensive brackishwater aquaculture, which is a dominant land-based aquaculture system in Indonesia, has experienced variable success in most farming locations in the country due to poor understanding of spatial assessment of environmental factors and rudimentary site selection criteria. Despite tremendous potential, the application of GIS and remote sensing in spatial assessment has tended to focus on Boolean (Crisp) logic that is often unable to effectively handle the complexity and spatial variability of key environmental factors for the development of aquaculture. This study explored the possibility of integrating fuzzy logic techniques into GIS and remote sensing technology to generate more robust mapping protocols in aquaculture, compensating for the disadvantages of the Crisp method. Two models were developed in two different provinces in Indonesia to spatially assess soil and hydrological constraints on extensive brackishwater aquaculture. The soil assessment focussed on acid sulfate soils (ASS) and sandy-textured sediments in Aceh, and the hydrological study focused on investigating important wave parameters that influence the suitability of coastal areas for siting extensive pond units in South Sulawesi. The study showed that fuzzy-based classification methods, integrated into the image analysis, was highly effective in identifying existing and potential pond areas for extensive brackishwater aquaculture compared to the best result of the commonly used Crisp method. By addition of one or more key environmental variables of ASS into the fuzzy-classified existing and potential ponds areas, a very robust predictive tool to identify potential ponds areas affected by ASS in Kembang Tanjung, Aceh was developed. A more detailed assessment of ASS developed in this study also successfully highlighted the severity of sandy-soils and identified them as another key soil variable that has and will severely impact on pond productivity. The second model developed by the study enables fuzzy logic to be integrated into GIS to predict the possible areas impacted by moderate to high energy wave conditions and possible ways of minimising their direct and indirect impacts. The models developed in this study were shown to work well in both study sites and can be applied elsewhere. The mapping outputs are easy to interpret even by stakeholders with no prior training in map reading. Overall, the models have the potential to reduce planning errors and to improve decision making in aquaculture provided that quality data sources are used.
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Soufian, Majeed. "Hard and soft computing techniques for non-linear modeling and control with industrial applications." Thesis, Manchester Metropolitan University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273053.

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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.

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Amer, Muhammad. "Extending Technology Roadmap through Fuzzy Cognitive Map-based Scenarios: The Case of the Wind Energy Sector of Pakistan." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/999.

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In this modern era, energy is a key element required for sustainable development and prosperity of a society. Pakistan is an energy deficient country facing problems due to the shortage of over 4000 MW of electricity. The national energy sector is heavily dependent on imported fossil-fuel resources. The energy crisis is negatively affecting all economic and business activities, and it is widely recognized as a severe obstacle to growth and poverty reduction in the country. Establishment of wind farms can help to overcome the energy crisis. In this research, a national level wind energy roadmap is developed through scenario planning. Multiple future scenarios are developed using the fuzzy cognitive maps (FCM) approach. This research has extended technology roadmapping through FCM-based scenario analysis. Building scenarios with FCM is a very new approach, and for the first time FCM-based scenarios are developed for the wind energy sector of Pakistan. Based on these multiple scenarios, a technology roadmap has been developed. This research approach is applied to the wind energy sector of Pakistan as a case study. This approach has been used to establish objectives and national targets of the roadmap. Then in a systematic way, critical roadmap barriers are identified against each scenario, and appropriate action items have been proposed to overcome barriers and promote deployment of wind energy projects in Pakistan. The objectives and targets of the roadmap have been translated into action items. The technology roadmap has four layers: strategic objectives, targets, barriers, and action items. Expert panels have been utilized to develop scenarios and technology roadmaps. Validation of this research is also carried out using experts. This new approach has helped to develop a robust roadmap and enabled anticipation of a wide range of possible future outcomes. This research fills an important gap by combining scenario planning and technology roadmapping techniques in future studies, and it has enhanced flexibility of the developed roadmap. Moreover, for the first time multiple and plausible FCM-based scenarios are developed, which combine the benefits of both qualitative and quantitative analysis. Moreover, the technology roadmap for the wind energy sector of Pakistan is developed with a comprehensive study of practical obstacles and barriers towards deployment of wind energy technology. The research findings suggest that policy, financial, economic, lack of competition with conventional power plants, and technical are the most critical barriers towards deployment of wind energy projects in the country. Appropriate action items required to overcome the roadmap barriers against each scenario are also proposed in the developed roadmap. The experts also assigned responsibilities for the key roadmap action items to the major stakeholders.
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Yang, Hsin-Lung, and 楊信龍. "The Fuzzy Logic for Review in the Industrial Applications." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44981282167595062760.

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碩士
元智大學
機械工程學系
96
The fuzzy theory nearly has 50 years history until now from its initial development. So far, it has covered wide ranges from industrial systems, plants, and products to home electrical appliances or even psycho-analysis and economical change tendency and so on so forth. During the nearly half century, Japan, Europe, and USA are all invested many efforts in fuzzy logic and its applications. We believe the future in fuzzy logic would have tremendous applications in every field. So, in this thesis, we try to look its history and introduce some examples in industrial products and finally see its future development.
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Tiwari, A., J. Knowles, E. Avineri, Keshav P. Dahal, and R. Roy. "Applications of Soft Computing." 2006. http://hdl.handle.net/10454/2291.

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Books on the topic "Fuzzy logic – Industrial applications – Congresses"

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EUSFLAT, 2003 (2003 Zittau Germany). Third Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2003: Proceedings : September 10-12, 2003, Zittau, Germany. Zittau: University of Applied Sciences, 2003.

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Fanelli, Anna Maria. Fuzzy Logic and Applications: 9th International Workshop, WILF 2011, Trani, Italy, August 29-31,2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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G, Okuno Hiroshi, and Ali Moonis, eds. New trends in applied artificial intelligence: 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2007, Kyoto, Japan, June 26-29, 2007 : proceedings. Berlin: Springer, 2007.

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Da, Ruan, ed. Fuzzy logic and intelligent technologies in nuclear science: Proceedings of the 1st International FLINS Workshop, Mol, Belgium, September 14-16, 1994. Singapore: World Scientific, 1994.

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Ruan, Da. Fuzzy Logic Foundations and Industrial Applications. Boston, MA: Springer US, 1996.

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Ruan, Da, ed. Fuzzy Logic Foundations and Industrial Applications. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1441-7.

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Bai, Ying, Hanqi Zhuang, and Dali Wang, eds. Advanced Fuzzy Logic Technologies in Industrial Applications. London: Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-469-4.

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Williams, Tom. The fuzzy logic market. Saratoga, Calif: Electronic Trend Publications, 1993.

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Heske, Ted. Fuzzy logic for real world design. San Diego, CA: Annabooks, 1996.

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International Conference on Fuzzy Logic and Applications (1997 Zikhron Yaʻaḳov, Israel). International Conference on Fuzzy Logic and Applications: Scientific program : Zikhron Yaakov, Israel May 18-21, 1997. Zichron Yaakov: Ministry of Science, 1997.

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Book chapters on the topic "Fuzzy logic – Industrial applications – Congresses"

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Latva-Käyrä, Kimmo. "Fuzzy Logic and SPC." In Industrial Applications of Soft Computing, 197–210. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1822-2_13.

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Harris, John. "Industrial Engineering." In An Introduction to Fuzzy Logic Applications, 122–69. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-9042-1_6.

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Friedman, Menahem, Ming Ma, and Abraham Kandel. "Fuzzy Derivatives and Fuzzy Cauchy Problems Using LP Metric." In Fuzzy Logic Foundations and Industrial Applications, 57–72. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1441-7_3.

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Ruan, Da. "Fuzzy Logic Applications in Nuclear Industry." In Fuzzy Logic Foundations and Industrial Applications, 313–27. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1441-7_15.

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Geyer-Schulz, Andreas, and Peter Kotauczek. "Industrial and commercial applications of fuzzy logic." In Fuzzy Logic in Artificial Intelligence, 190–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56920-0_22.

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Myllymäki, Pekka, and Jussi Poutiainen. "Fuzzy Logic Control of a Sintering Plant." In Industrial Applications of Soft Computing, 65–78. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1822-2_5.

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Branciforte, Marco, Riccardo Caponetto, Mario Lavorgna, and Luigi Occhipinti. "Industrial Applications of Soft Computing." In Lectures on Soft Computing and Fuzzy Logic, 33–42. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1818-5_3.

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Tanaka, Hideo. "Possibility Model and its Applications." In Fuzzy Logic Foundations and Industrial Applications, 93–110. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1441-7_5.

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Hellendoorn, Hans. "Some Application Examples of Fuzzy Set Theory." In Fuzzy Logic Foundations and Industrial Applications, 201–24. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1441-7_10.

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Yager, Ronald R. "A Unified View of Case Based Reasoning and Fuzzy Modeling." In Fuzzy Logic Foundations and Industrial Applications, 5–26. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1441-7_1.

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Conference papers on the topic "Fuzzy logic – Industrial applications – Congresses"

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Nithyanandam, J., K. Palanikumar, and Sushil Lal Das. "Fuzzy Rule Based Modeling for Surface Roughness in Machining of Titanium Alloy Using Nano Coated Carbide Inserts." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51458.

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Titanium and its alloys are used in many industrial and engineering applications because of their good properties, such as high strength to weight ratio, excellent fracture and corrosion resistance. The major application of titanium has been in the aerospace industry. When turning of titanium alloys with conventional tools, the tool wear rate increases, because of high chemical reactivity and strong adhesion between the tool and work piece materials. The nano coated carbide cutting tool is used for the turning experiment. The cutting parameter for the experimental works are cutting speed, feed rate, nose radius, and depth of cut. Fuzzy logic modeling is used for the prediction of surface roughness in machining of titanium alloy. From the results, the Fuzzy logic model is the best suited method for modeling the turning parameters of titanium alloy by using Nanocoated carbide tools.
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Shi, Linda Z., and Mohamed B. Trabia. "Design and Tuning of Distributed Importance-Based Fuzzy Logic Controller for Two-Link Rigid-Flexible Manipulator." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41971.

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Fuzzy logic control has been widely used in many industrial processes due to its computationally efficient and robust characteristics. In many applications, verbalization of expert-knowledge can be easily used to design a fuzzy logic controller (FLC). On the other hand, other applications with many variables and complex mathematical model offer challenges to fuzzy logic control. Multi-link flexible manipulators belong to this category. An earlier work, [1], presented a distributed importance-based FLC for a single-link flexible manipulator. This paper extends this idea to a two-link rigid-flexible manipulator that moves in a vertical plane where the gravity field is active. The structure of the proposed controller is based on evaluating the importance degrees of the variables of the system, over its range of operation, to consider the coupling effects between the rigid and the flexible links. Variables with higher importance degrees are grouped together while variables with lesser importance degrees may be deleted to simplify the design of the controller. After determining the importance degrees of the variables, a distributed controller composed of four two-input one-output FLC’s is created. Unlike the single-link flexible manipulator, the fuzzy rules of the distributed FLC for the two-link rigid-flexible manipulator cannot be written by an expert based on intuition and observation of the inertial system due to the complexity of the manipulator and the coupling effect of its variables. To solve this problem, an importance-based linear controller that has the same input-output structure as that of distributed importance-based FLC is constructed to help write the fuzzy rules of the distributed FLC. Fuzzy rules of the distributed FLC are then selected to mimic the performance of the corresponding linear controllers. To compare the performance of the distributed importance-based FLC with that of importance-based linear controller, these two controllers are tuned using nonlinear programming by varying the gains of the importance-based linear controller and the parameters of membership functions of the variables in the distributed importance-based FLC. Robustness of each of the controllers after tuning is tested by varying the payload of the manipulator. The two importance-based controllers are simulated and compared.
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Barelli, Linda, Gianni Bidini, and Rube´n Arce. "Fuzzy Logic Regulator for the Performance Improvement and the Energy Consumption Reduction of an Industrial Chiller." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41910.

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Nowadays producers of household and industrial appliances need to optimize the efficiency of their products to diminish electric consumptions. In the case of refrigerators, the compressor is the component characterized by the highest electric absorption. The aim of the present study is to develop a compressor control algorithm for an industrial chiller to improve the performances and reduce the energy consumption. Results obtained by the authors in previous studies, encourage to the implementation of the fuzzy logic for regulator design. In particular a refrigerator for pastry shop was considered as the object of the study. A fuzzy controller was developed for the particular application by software simulation. The controller has greater performances of the PID and optimal linear regulators designed by the author for the same system. Subsequently a fuzzy adaptive procedure was defined to generalize the control algorithm, and allow the implementation of the controller to different refrigerators.
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Kulkarni, Arun D., G. B. Giridhar, and Praveen Coca. "Neural-network-based fuzzy logic decision systems." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188914.

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Manjang, Salama, and Yusri Akil. "Thermal Plants Optimization Using Fuzzy Logic Controller." In 2006 1ST IEEE Conference on Industrial Electronics and Applications. IEEE, 2006. http://dx.doi.org/10.1109/iciea.2006.257064.

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Cirstea, M. N., J. G. Khor, and C. Marinescu. "Fuzzy Logic Controller for Hybrid Electric Vehicle Applications." In Proceedings of the IEEE International Symposium on Industrial Electronics. IEEE, 2005. http://dx.doi.org/10.1109/isie.2005.1529153.

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Mastacan, Lucian, and Catalin-Constantin Dosoftei. "Fuzzy logic controller design and implementation for industrial applications." In 2014 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2014. http://dx.doi.org/10.1109/icepe.2014.6969873.

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Hor, MawKae, and Hui L. Lu. "Neural-network-based fuzzy logic control system with applications on compliant robot control." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188915.

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Razaghian, Farhad, and Zahra Soleimani Tazekandi. "A novel fuzzy logic technique in automation design of industrial circuits." In Applications (ISIEA 2009). IEEE, 2009. http://dx.doi.org/10.1109/isiea.2009.5356364.

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Bobyr, M. V., A. Nasser Abduldaiem, and Mohammed A. Abduljabbar. "Cooled cutter control algorithm based on fuzzy logic." In 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2017. http://dx.doi.org/10.1109/icieam.2017.8076168.

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