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Control of Trms using Adaptive Neuro Fuzzy Inference System (ANFIS)
2020 International Conference on System, Computation, Automation and Networking (ICSCAN), 2020This paper gives approach about the designing of ANFIS controller to control a TRMS to track the desired trajectory and make the system stable. The stages of development of a two input ANFIS model were presented. It shows the designing of controller is easy and it reduces the time and memory.
K. Kalyani, S. Kanagalakshmi
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Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)
Physics in Medicine and Biology, 2005The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage.
Manish, Kakar +4 more
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Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS)
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011This paper applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. Landslide-related factors such as slope, soil texture, wood type, lithology and density of lineament were extracted from topographic, soil ...
Jaewon Choi +9 more
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Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering
Computers and Geotechnics, 2012Abstract This paper presents a review of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in current use for geotechnical engineering-based studies, as well as some applications employed in resonant column testing, triaxial testing, and liquefaction triggering. Over the last few years, ANFIS has been used in many geotechnical engineering problems. A
Abdülkadir Çevik, Candan Gökçeoglu
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Detection of forearm movements using wavelets and Adaptive Neuro-Fuzzy Inference System (ANFIS)
2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014In this paper, a technique to classify seven different forearm movements using surface electromyography (sEMG) data which were received from 8 able bodied subjects was proposed. A 2-channel sEMG system was used for data acquisition and recording, then this raw electromyography (EMG) signals were applied to the wavelet denoising.
Seyit Ahmet Guvenc +2 more
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Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
2014Accurate vehicle classification and traffic composition data are an important traffic performance measures which are used in many transportation applications. In this paper, an attempt is made to develop a model to classify the vehicles into five categories: light commercial vehicle, car/jeep/van, two-axle truck/bus, three-axle truck, and multi-axle ...
Akhilesh Kumar Maurya +1 more
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An Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to control of robotic manipulators
1998In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) is used for the controlling of a commercial robot manipulator. A Microbot [1] with three degrees of freedom is utilized to evaluate the proposed methodology. A decentralized ANFIS controller is used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematics ...
Ali Zilouchian +2 more
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Prediction of Diabetes using Adaptive Neuro Fuzzy Inference System (ANFIS)
Asian Journal of Research in Social Sciences and Humanities, 2016In this work a systematic architecture and procedure of Adaptive Neuro Fuzzy Inference System (ANFIS) is presented for data mining classification model to predict the Pima diabetes dataset and compare the model errors with Artificial Neural Network (ANN) model.
B. Thanga Parvathi, S. Mercy Shalinie
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Optimization of Photosynthetic Rate Parameters using Adaptive Neuro-Fuzzy Inference System (ANFIS)
2017 International Conference on Computer and Applications (ICCA), 2017Crop growth is greatly affected by light intensity, temperature and CO 2 concentration. The combinations of these factors are considered in growing crops. In this study, a system was developed using adaptive neuro-fuzzy inference system for the prediction of the photosynthetic rate of lettuce crop based on the temperature, light intensity and CO 2 . A
Ira C. Valenzuela +3 more
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Fuzzy membership function design: An adaptive neuro-fuzzy inference system (ANFIS) based approach
2021 International Conference on Computer Communication and Informatics (ICCCI), 2021In recent times, neuro-fuzzy approaches have been widely used for solving real-world complex problems. Adaptive neuro-fuzzy inference system (ANFIS) is one of the prominent models in this field of soft computing. This model combines both the fuzzy and neural approaches to represent non-linear and ill-defined problems more preciously.
Monika Kabir, Mir Md. Jahangir Kabir
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