Results 181 to 190 of about 17,074 (218)
Application of Taguchi Grey-Based ANFIS model for prediction of process parameters in fused deposition modelling of PETG. [PDF]
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Adaptive Neuro Fuzzy Inference System (ANFIS) based wildfire risk assessment
Journal of Experimental & Theoretical Artificial Intelligence, 2019ABSTRACTWildfires are extremely destructive disasters that cause significant loss of lives, forest cover and wildlife. This is due to their uncontrolled, erratic, rapid spread and behaviour. The incidence of wildfires is expected to increase worldwide because of Global Warming.
Harkiran Kaur, Sandeep K. Sood
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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
Ali Firat Cabalar +2 more
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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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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 B. Khalifa, Hichem Frigui
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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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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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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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Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)
Applied Mechanics and Materials, 2015The paper is dedicated to analyze the modern expert systems to assess the technical condition of power stations and substations high-voltage equipment. The main problems of modern expert systems and their possible solutions are determined. As the structure and their basic construction principles are considered. Also this paper proposes an algorithm for
Alexandra Khalyasmaa +2 more
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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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