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MI-ANFIS: A multiple instance Adaptive Neuro-Fuzzy Inference System
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015We introduce a novel adaptive neuro-fuzzy architecture based on the framework of Multiple Instance Fuzzy Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS) [1] that is designed to handle reasoning with multiple instances (bags of instances) as input ...
Amine Ben Khalifa, Hichem Frigui
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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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Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival
International Conference on Fuzzy Systems, 2010Medical prognosis is the prediction of the future course and outcome of a disease and an indication of the likelihood of recovery from that disease. Soft-computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behaviour.
Hazlina Hamdan, Jonathan M. Garibaldi
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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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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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