Relevant bibliographies by topics / Adaptive Network Based Fuzzy Inference System (ANFIS)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Adaptive Network Based Fuzzy Inference System (ANFIS)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Adaptive Network Based Fuzzy Inference System (ANFIS)"

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Jang, J. S. R. "ANFIS: adaptive-network-based fuzzy inference system." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 3 (1993): 665–85. http://dx.doi.org/10.1109/21.256541.

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CHAI, YUANYUAN, and LIMIN JIA. "CHOQUET INTEGRAL–OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION." International Journal of Computational Intelligence and Applications 10, no. 01 (2011): 15–34. http://dx.doi.org/10.1142/s1469026811002970.

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In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral–OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neura
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Gao, Ming Ming, and Liang Shan. "The Study of System Model Based on Fuzzy Inference and Neural Network." Applied Mechanics and Materials 197 (September 2012): 547–52. http://dx.doi.org/10.4028/www.scientific.net/amm.197.547.

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For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. Thi
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Yeom, Chan-Uk, and Keun-Chang Kwak. "Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach." Applied Sciences 10, no. 23 (2020): 8495. http://dx.doi.org/10.3390/app10238495.

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We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, m
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Siddiquee, Mahfuzur Rahman, Naimul Haider, and Rashedur M. Rahman. "Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System." International Journal of Fuzzy System Applications 4, no. 4 (2015): 31–69. http://dx.doi.org/10.4018/ijfsa.2015100103.

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One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Dist
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EL-BAKRY, M. Y. "A STUDY OF K–P INTERACTION AT HIGH ENERGY USING ADAPTIVE FUZZY INFERENCE SYSTEM INTERACTIONS." International Journal of Modern Physics C 15, no. 07 (2004): 1013–20. http://dx.doi.org/10.1142/s0129183104006467.

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Adaptive Network Fuzzy Inference System (ANFIS) is an artificial intelligence (AI)-based technique that proved efficient in a variety of problems such as classification, recognition and modeling of complex systems. This paper utilizes the adaptive network fuzzy inference system to model the K–P interactions. The ANFIS-based K–P model simulates the multiplicity distribution of charged pions at different high energies. The results showed very accurate fitting to the experimental data recommending it to be a good alternative to other theoretical techniques.
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Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.

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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated control
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Badvaji, Bhumika, Raunak Jangid, and Kapil Parikh. "PERFORMANCE ANALYSIS ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED MPPT CONTROLLER FOR DC-DC CONVERTER FOR STANDALONE SOLAR ENERGY GENERATION SYSTEM." International Journal of Technical Research & Science 7, no. 06 (2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.

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This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of converter performance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is used. In order to demonstrate the proposed ANFIS controller abilities to follow the reference voltage and current, its performance is simulated and compared with Artificial Intellige
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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptatio
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Rathnayake, Namal, Upaka Rathnayake, Tuan Linh Dang, and Yukinobu Hoshino. "A Cascaded Adaptive Network-Based Fuzzy Inference System for Hydropower Forecasting." Sensors 22, no. 8 (2022): 2905. http://dx.doi.org/10.3390/s22082905.

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Hydropower stands as a crucial source of power in the current world, and there is a vast range of benefits of forecasting power generation for the future. This paper focuses on the significance of climate change on the future representation of the Samanalawewa Reservoir Hydropower Project using an architecture of the Cascaded ANFIS algorithm. Moreover, we assess the capacity of the novel Cascaded ANFIS algorithm for handling regression problems and compare the results with the state-of-art regression models. The inputs to this system were the rainfall data of selected weather stations inside t
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Dissertations / Theses on the topic "Adaptive Network Based Fuzzy Inference System (ANFIS)"

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Weeraprajak, Issarest. "Faster Adaptive Network Based Fuzzy Inference System." Thesis, University of Canterbury. Mathematics and Statistics, 2007. http://hdl.handle.net/10092/1234.

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It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the pa
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Xu, Andong. "Flexible adaptive-network-based fuzzy inference system." Diss., Online access via UMI:, 2006.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2006.<br>Includes bibliographical references.
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases
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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.

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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural
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Jahankhani, Pari. "Development of a decision support framework for electroencephalography signals based on an adaptive fuzzy inference neural network system." Thesis, University of Westminster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507837.

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Cho, B. "Control of a hybrid electric vehicle with predictive journey estimation." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2589.

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Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed fr
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Guruprasad, K. R. "Model Reference Learning Control Using ANFIS." Thesis, 1996. https://etd.iisc.ac.in/handle/2005/1714.

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Guruprasad, K. R. "Model Reference Learning Control Using ANFIS." Thesis, 1996. http://etd.iisc.ernet.in/handle/2005/1714.

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Popoola, Olawale Muhammed. "Adaptive neuro-fuzzy inference system (ANFIS)-based modelling of residential lighting load profile." 2015. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001770.

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D. Tech. Electrical Engineering.<br>Aims of this study is to develop a residential customers' lighting profile ANFIS-based model. This model is expected to address lighting load usage estimation in relation to the dynamic occupancy presence in a residential dwelling, which will take into account the climatic condition (natural lighting) of such an environment (e.g. South Africa) and its income. The objectives are as follows: 1. Develop an ANFIS-based residential lighting load profile model for middle income, low income and high-income earners. 2. Error reduction in residential lighting demand
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Chen, Chi-Ming, and 陳啟銘. "Adaptive Network-Based Fuzzy Inference System for Driving Status Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/49789636410910847334.

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碩士<br>長庚大學<br>電機工程研究所<br>95<br>This thesis proposes an intelligent system which analyzes driving status. The system infrastructure is utilized two fixed cameras on the host vehicle. One is used to capture driver’s image in order to analyze driver’s sight line, and the other is used to capture image of road ahead for analyzing driving pattern. In the section of driver’s image, it’s necessary to utilize AdaBoost algorism to recognize face and then get the positions of eyes, nose and lips to diagnose the angles of driver’s head and driver’s sight line. In the section of road image, it’s used edge
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Book chapters on the topic "Adaptive Network Based Fuzzy Inference System (ANFIS)"

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Chen, Seng-Chi, Dinh-Kha Le, and Van-Sum Nguyen. "Adaptive Network-Based Fuzzy Inference System (ANFIS) Controller for an Active Magnetic Bearing System with Unbalance Mass." In AETA 2013: Recent Advances in Electrical Engineering and Related Sciences. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41968-3_44.

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Mohd Yusof, Nur Hidayah, Yan Chai Hum, Nur Azah Hamzaid, and Khin Wee Lai. "Adaptive Network Based Fuzzy Inference System (ANFIS) for an Active Transfemoral Prosthetic Leg by Using In-Socket Sensory System." In IFMBE Proceedings. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7554-4_49.

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Brahim, Kais, and Andreas Zell. "ANFIS-SNNS: Adaptive Network Fuzzy Inference System in the Stuttgart Neural Network Simulator." In Fuzzy-Systems in Computer Science. Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-86825-1_9.

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Avci, Engin, Ibrahim Turkoglu, and Mustafa Poyraz. "Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_72.

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Saikia, Darshana, and Jiten Chandra Dutta. "Adaptive Network Based Fuzzy Inference System for Early Diagnosis of Dengue Disease." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3770-2_68.

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Jamili, N. S. B., Mohd Rafi Adzman, Wan Syaza Ainaa Wan Salman, M. H. Idris, and M. Amirruddin. "Fault Localization and Detection in Medium Voltage Distribution Network Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5281-6_74.

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Mishra, Pratishtha, and Pijush Samui. "Reliability Analysis of Retaining Wall Using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Lecture Notes in Civil Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6370-0_48.

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Soozanchi-K., Zohreh, Mohammad-R. Akbarzadeh-T., Mahdi Yaghoobi, and Saeed Rahati. "Speaker Verification System Using a Hierarchical Adaptive Network-Based Fuzzy Inference Systems (HANFIS)." In Artificial Intelligence in Theory and Practice III. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15286-3_23.

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Li, Xi, and Byung-Jae Choi. "Design of Adaptive Network-Based Fuzzy Inference System for Obstacle Avoidance of Mobile Robot." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05573-2_8.

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Nourani, Vahid, Fahreddin Sadikoglu, Nardin Jabbarian Paknezhad, and Elnaz Sharghi. "Prediction Intervals for the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) via the LUBE Method." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68004-6_1.

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Conference papers on the topic "Adaptive Network Based Fuzzy Inference System (ANFIS)"

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Faradilla, Arnes, and Taufik Djatna. "The Prevention Stroke for High-Risk Patients Using Prediction and Treatment Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)." In 14th International Seminar on Industrial Engineering and Management. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-qk2n23.

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Stroke is the second factor of mortality in the world. According to the World Health Organization (WHO), stroke is an acute brain dysfunction. The effects of stroke are disability and mortality. Therefore, this is a concern for world health. In early 2019, the Pandemic Covid-19 attacked the world and caused many mortalities. Especially, people who have complications with diseases such as heart attack, stroke, and asthma. The purpose of this research is to predict stroke diseases with input parameters (age, glucose level, heart rate, and BMI) and to test the accuracy of the system. Moreover, an
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Vladov, Serhii, Victoria Vysotska, Valerii Sokurenko, Oleksandr Muzychuk, Vasyl Lytvyn, and Vitalii Danylyk. "Cyber-Physical System for Synthesizing Adaptive Fuzzy Controllers Based on Modified ANFIS Neuro-Fuzzy Network." In 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT). IEEE, 2024. https://doi.org/10.1109/csit65290.2024.10982649.

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Zhang, Xuewei, Long Long, Xuancheng Liu, Di Xu, Qiliang Yang, and Jidong Ge. "Damage Prediction of Underground Engineering by Adaptive Network Based Fuzzy Inference System." In 2024 IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2024. https://doi.org/10.1109/iccsse63803.2024.10823774.

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G., Nivedhitha, Pranav Karthikeyan, and K. R. M. Vijaya Chandrakala. "Adaptive Cruise Control System in Transportation Systems using Artificial Neural Network based Fuzzy Inference System." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984772.

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Amiralaei, M. R., M. Partovibakhsh, and H. Alighanbari. "Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) in Aerodynamics Prediction of Low-Reynolds-Number Flapping Motion." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40679.

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The objective of the present study is to develop an Adaptive Network-based Fuzzy Inference System (ANFIS) model to predict the unsteady lift coefficients of an airfoil. The airfoil performs a flapping motion in Low-Reynolds-Number (LRN) flow regime. Computational Fluid Dynamics (CFD) simulations of the flow field are conducted and the corresponding unsteady lift coefficients are used as the input data to ANFIS. The results show that the ANFIS model is capable of predicting the lift coefficients with very good accuracy, which could be of great value in the preliminary design stages.
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Balonji, Serge, I. P. Okokpujie, and L. K. Tartibu. "Analysis of Surface Roughness in End-Milling of Aluminium Using an Adaptive Network-Based Fuzzy Inference System." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-68468.

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Abstract End-milling is considered one of the well-known cutting processes that machines surface using cutter tools that operate at relatively high speed. In order to manufacture mechanical parts, existing studies suggest that minimum roughness could be obtained if the radial and axial depth of the cut, the feed rate, and the spindle speed is adequately adjusted. This study considers the results of an experimental investigation conducted on 30 samples of aluminum alloy AL-6061 using a CNC machine and analyzes the relationship between independent input parameters, taken within well-defined rang
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Samanta, B. "Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84643.

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A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The
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MonirVaghefi, Hossein, Mohsen Rafiee Sandgani, and Mahdi Aliyari Shoorehdeli. "Interval Type-2 Adaptive Network-based Fuzzy Inference System (ANFIS) with Type-2 non-singleton fuzzification." In 2013 13th Iranian Conference on Fuzzy Systems (IFSC). IEEE, 2013. http://dx.doi.org/10.1109/ifsc.2013.6675612.

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Olayode, O. I., L. K. Tartibu, and M. O. Okwu. "Application of Adaptive Neuro-Fuzzy Inference System Model on Traffic Flow of Vehicles at a Signalized Road Intersections." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70956.

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Abstract In recent years, most traffic accidents and congestions usually occur at road intersections in urban areas where the vehicle speed is high. This has necessitated the need for intelligent road transport systems and high-level algorithms to unravel the problem. In this study, the South Africa Road transportation system has been used as a case study to address traffic flow solutions at signalized road intersections using traffic flow variables such as traffic density, speed of vehicles, and traffic volume as decision variables. This paper focuses on using a hybrid creative algorithm base
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Letha, Shimi Sudha, Tilak Thakur, Jagdish Kumar, Dnyaneshwar Karanjkar, and Santanu Chatterji. "Design and Real Time Simulation of Artificial Intelligent Based MPP Tracker for Photo-Voltaic System." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37967.

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This paper presents an Artificial Intelligent based Maximum Power Point Tracking (MPPT) of a photo-voltaic system implementation using dSPACE 1104. The paper also proposes a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) / Constant Voltage Tracker (CVT) for a photovoltaic (PV) powered multilevel inverter which requires a fixed constant dc voltage at its input. The MPPT algorithms viz. perturb and observe, incremental conductance, neural network, ANFIS and ANFIS/CVT have been designed and implemented on laboratory prototype. The modeling of various MPPT algorithms have been done on MATLAB/
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