Relevant bibliographies by topics / Adaptive neuro-fuzzy (ANFIS)

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

Academic literature on the topic 'Adaptive neuro-fuzzy (ANFIS)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Adaptive neuro-fuzzy (ANFIS)"

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Yeom, Chan-Uk, and Keun-Chang Kwak. "Performance Comparison of ANFIS Models by Input Space Partitioning Methods." Symmetry 10, no. 12 (2018): 700. http://dx.doi.org/10.3390/sym10120700.

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In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a
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Moyo, Dr Samuel. "OPTIMIZING ADAPTIVE NEURO-FUZZY SYSTEMS FOR ENHANCED PHISHING DETECTION." International Journal of Intelligent Data and Machine Learning 2, no. 05 (2025): 8–13. https://doi.org/10.55640/ijidml-v02i05-02.

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Phishing attacks continue to pose a significant and evolving threat to individuals and organizations, leading to substantial financial losses and compromising sensitive information [1]. Traditional detection methods, often reliant on static blacklists or rule-based systems, struggle to keep pace with the dynamic nature and increasing sophistication of these scams. This article explores the critical role of parameter optimization within Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for developing intelligent and robust phishing detection capabilities. ANFIS, by combining the learning capabilit
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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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Tahour, Ahmed, Hamza Abid, and Ghani Aissaoui. "Adaptive neuro-fuzzy controller of switched reluctance motor." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 23–34. http://dx.doi.org/10.2298/sjee0701023t.

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This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy contro
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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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Sabet, Masumeh, Mehdi Naseri, and Hosein Sabet. "Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System." Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 42, no. 1 (2010): 159–67. http://dx.doi.org/10.2478/v10060-008-0074-6.

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Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy infere
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Mindit Eriyadi, S.Pd, M.T. "PERANCANGAN DAN SIMULASI BASIC ENGINE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)." TEMATIK 2, no. 2 (2015): 105–13. http://dx.doi.org/10.38204/tematik.v2i2.76.

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Abstrak : Adaptif Neuro Fuzzy Inference System ( ANFIS ) merupakan salah satu variasi bentuk dari fuzzy. Untuk dapat menggunakan ANFIS, dapat dibuat engine ANFIS yang berfungsi menjalankan logika fuzzy yang dirancang. Perancangan dan simulasi basic engine ANFIS ini bertujuan untuk merancang sebuah basic engine ANFIS dan menguji performansinya dalam sebuah simulasi. Perancangan dan pengujian simulasi dilakukan dengan menggunakan perangkat lunak MATLAB 7.5.0 dengan fitur anfis editor. Dari hasil pengujian simulasi basic engine ANFIS yang dirancang, didapatkan hasil bahwa basic engine yang diranc
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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and labora
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Mujiarto, Mujiarto, Asari Djohar, Mumu Komaro, et al. "Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 293–99. https://doi.org/10.11591/ijeecs.v13.i1.pp293-299.

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In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.
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Nath, Amitabha, Fisokuhle Mthethwa, and Goutam Saha. "Runoff estimation using modified adaptive neuro-fuzzy inference system." Environmental Engineering Research 25, no. 4 (2019): 545–53. http://dx.doi.org/10.4491/eer.2019.166.

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Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindr
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Dissertations / Theses on the topic "Adaptive neuro-fuzzy (ANFIS)"

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Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.

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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs. Effective control for distillation systems, which are one of the importa
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Hamdan, Hazlina. "An exploration of the adaptive neuro-fuzzy inference system (ANFIS) in modelling survival." Thesis, University of Nottingham, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594875.

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Medical prognosis is the prediction of the future course and outcome of a disease and an indication of the likelihood of recovery from that disease. Prognosis is important because it is used to guide the type and intensity of the medication administered to patients. Patients are usually concerned with how long they will survive after diagnosis. Survival analysis describes the analysis of data that corresponds to the time from when an individual enters a study until the occurrence of some particular event or end-point. It is concerned with the comparison of survival curves for different combina
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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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Khanfar, Ahmad A. "Forecasting failure of information technology projects using an adaptive neuro-fuzzy inference system." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2262.

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The role of information technology (IT) applications has become critical for organisations in various sectors such as education, health, finance, logistics, manufacturing and project management. IT applications provide many advantages at strategic, management and operational levels, and the investment in IT applications is therefore growing; however, the failure rate of IT projects is still high, despite the development of theories, methodologies and frameworks for IT project management in recent decades. The consequences of failure of an IT project can be devastating, and can threaten the exi
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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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Jain, Aakanksha. "Application of Artificial Intelligence Techniques in the Prediction of Industrial Outfall Discharges." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39812.

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Artificial intelligence techniques have been widely used for prediction in various areas of sciences and engineering. In the thesis, applications of AI techniques are studied to predict the dilution of industrial outfall discharges. The discharge of industrial effluents from the outfall systems is broadly divided into two categories on the basis of density. The effluent with density higher than the water receiving will sink and called as negatively buoyant jet. The effluent with density lower than the receiving water will rise and called as positively buoyant jet. The effluent discharge in the
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Chotikorn, Nattapong. "Implementations of Fuzzy Adaptive Dynamic Programming Controls on DC to DC Converters." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505139/.

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DC to DC converters stabilize the voltage obtained from voltage sources such as solar power system, wind energy sources, wave energy sources, rectified voltage from alternators, and so forth. Hence, the need for improving its control algorithm is inevitable. Many algorithms are applied to DC to DC converters. This thesis designs fuzzy adaptive dynamic programming (Fuzzy ADP) algorithm. Also, this thesis implements both adaptive dynamic programming (ADP) and Fuzzy ADP on DC to DC converters to observe the performance of the output voltage trajectories.
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Kiani, Mavi Neda. "Forecasting project success in the construction industry using multi-criteria decision-making tools and adaptive neuro fuzzy inference system." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2024. https://ro.ecu.edu.au/theses/2791.

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The construction industry plays a significant role in the development of economies. This industry in Australia contributed around 20% Australian Trade and Investment Commission (2023) to its gross domestic product (GDP) of over US$1.80 trillion (approximately AUD 2.85 trillion) (OECD, 2023).The federal budget for 2022–23 allocates AUD 17.9 billion over a decade towards major infrastructure projects, encompassing substantial funding for nationwide road and rail projects. The overall investment in major public infrastructure is anticipated to surpass AU$218 billion from 2021 to 2025. Approximate
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Spadotto, Marcelo Montepulciano [UNESP]. "Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/87176.

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Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-07-29Bitstream added on 2014-06-13T19:08:09Z : No. of bitstreams: 1 spadotto_mm_me_bauru.pdf: 1459647 bytes, checksum: c67d870286e648ad917f7e25b8b18d56 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materia
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Arsava, Kemal Sarp. "Modeling, Control and Monitoring of Smart Structures under High Impact Loads." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/105.

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In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a dif
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Books on the topic "Adaptive neuro-fuzzy (ANFIS)"

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Neelanarayanan, ed. Multi-step Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS). Association of Scientists, Developers and Faculties, 2014.

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Book chapters on the topic "Adaptive neuro-fuzzy (ANFIS)"

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Maurya, Akhilesh Kumar, and Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2220-0_11.

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Maurya, Akhilesh Kumar, and Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro Fuzzy Inference System (ANFIS)." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2220-0_54.

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Aydin, Olgun, and Elvan Aktürk Hayat. "Estimation of Housing Demand with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)." In The Impact of Globalization on International Finance and Accounting. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68762-9_49.

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Srivastava, Amit Kumar, Pooja, and Tanveer Jahan Siddiqui. "An adaptive neuro-fuzzy inference system (ANFIS) for sentiment analysis classification." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003635680-54.

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Sharma, Jitender, Sonia, Karan Kumar, Zakaria Boulouard, Adedapo Paul Aderemi, and Celestine Iwendi. "Utilizing Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Intrusion Detection Systems." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94620-2_2.

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Faycal, Djebbas, Zeddouri Aziez, and Belila Djilani. "Prediction of the Porosity Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Technique." In Springer Series in Geomechanics and Geoengineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1964-2_87.

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Lounis, B., O. Raaf, L. Bouchemakh, and Y. Smara. "SeaWIFS Coastal Waters Mapping Using an Adaptive Neuro-fuzzy Inference System (ANFIS)." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4776-4_50.

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Zilouchian, Ali, David W. Howard, and Timothy Jordanides. "An Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to control of robotic manipulators." In Tasks and Methods in Applied Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_424.

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Adedeji, P. A., S. O. Masebinu, S. A. Akinlabi, and N. Madushele. "Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources." In Optimization Using Evolutionary Algorithms and Metaheuristics. CRC Press, 2019. http://dx.doi.org/10.1201/9780429293030-7.

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Hakim, Seyed Jamalaldin S., and H. Abdul Razak. "Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS)." In Topics in Modal Analysis I, Volume 5. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2425-3_37.

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Conference papers on the topic "Adaptive neuro-fuzzy (ANFIS)"

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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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S, Edy Victor Haryanto, Nita Sari Br Sembiring, Mikha Dayan Sinaga, and Noprita Elisabeth Sianturi. "Implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) Algorithm for Customer Credit Prediction." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903682.

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Riananda, Dimas Pristovani, Salsabila Ika Yuniza, Ryan Yudha Adhitya, et al. "System Protection Analysis on 1kW Solar Charge Hybrid Inverter Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11080910.

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Amin, Ahmad Faishol, Ronny Cahyadi Utomo, and Khoirul Azis Rifa’i. "Comparing Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Auto-Cooling System in Generator Rotor Straightening." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747980.

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Mehrabi, Mehdi, Mohsen Sharifpur, and Josua P. Meyer. "Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids." In ASME 2012 Third International Conference on Micro/Nanoscale Heat and Mass Transfer. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/mnhmt2012-75023.

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By using on Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as experimental data, a model was established for the prediction of the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In the ANFIS the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. In the development of the model, the empirical data was divided into train and test sections. The ANFIS network was instructed by eighty percent of the experimental data and the remaining data (t
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Liu, Shi, and Liangsheng Qu. "Application of Adaptive Neuro-Fuzzy Inference System in Field Balancing." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80367.

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The field balancing of flexible rotors is one of the key techniques to reduce vibration of large rotating machinery. Although in recent decades the balancing theory has been thoroughly studied and various balancing techniques have been well developed, the present balancing methods are still remain for further improvements in accuracy and efficiency. Firstly, most balancing methods need large numbers of trial runs to obtain the vibration responses of trial weights in different correcting planes. Secondly, the vibration response in each measured section is always taken from a single sensor, and
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Albakkar, A., and O. P. Malik. "Adaptive neuro-fuzzy controller based on simplified ANFIS network." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6344842.

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Smaili, Ahmad, Fouad Mrad, and Hadi Maamoun. "Neuro-Fuzzy Control of Smart Flexible Mechanisms." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48366.

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This paper presents an analytical investigation in which a controller based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is designed and implemented to control the vibrations of a flexible mechanism system with smart coupler link. The most dominant vibration mode of the mechanism is identified and the controller is then designed to reduce the effect of this mode on the response of the mechanism system. The proposed control algorithm is implemented on a mechanism system with a thin plate-type piezoceramic actuator bonded to the coupler link surface at the high strain location corresponding
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Kharola, Ashwani, Ritvik Dobriyal, Rakesh Chandmal Sharma, Neeraj Sharma, Ashwini Sharma, and Anuj Raturi. "Hydrodynamic Flow Characteristics Prediction for Bluff Body Wake via Novel Adaptive Neuro-Fuzzy Controller Avoiding Fuzzy Rule Explosion." In Automotive Technical Papers. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-5081.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This study analyses the effect of Reynolds number (&lt;i&gt;Re&lt;/i&gt;) and bluff body shape (quantified by shape factor &lt;i&gt;SF&lt;/i&gt;) variation on various hydrodynamic characteristics of unsteady bluff body flow, such as Strouhal number, maximum lift coefficient, and mean drag coefficient. The study initially examines a relationship among these characteristics and further utilizes artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) controllers for their precise prediction. The
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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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