Relevant bibliographies by topics / NEURO-FUZZY ANFIS

Contents

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

Academic literature on the topic 'NEURO-FUZZY ANFIS'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'NEURO-FUZZY ANFIS.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "NEURO-FUZZY ANFIS"

1

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
4

Sadeghi-Niaraki, Abolghasem, Ozgur Kisi, and Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods." PeerJ 8 (August 14, 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.

Full text
Abstract:
This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 s
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
10

Kong, Lingkun, Dewang Chen, and Ruijun Cheng. "WRNFS: Width Residual Neuro Fuzzy System, a Fast-Learning Algorithm with High Interpretability." Applied Sciences 12, no. 12 (2022): 5810. http://dx.doi.org/10.3390/app12125810.

Full text
Abstract:
Although the deep neural network has a strong fitting ability, it is difficult to be applied to safety-critical fields because of its poor interpretability. Based on the adaptive neuro-fuzzy inference system (ANFIS) and the concept of residual network, a width residual neuro-fuzzy system (WRNFS) is proposed to improve the interpretability performance in this paper. WRNFS is used to transform a regression problem of high-dimensional data into the sum of several low-dimensional neuro-fuzzy systems. The ANFIS model in the next layer is established based on the low dimensional data and the residua
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "NEURO-FUZZY ANFIS"

1

Dalecký, Štěpán. "Neuro-fuzzy systémy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.

Full text
Abstract:
The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system.  The thesis is aimed
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
4

Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
5

Lima, Fábio. "Estimador neuro-fuzzy de velocidade aplicado ao controle vetorial sem sensores de motores de indução trifásicos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-20092011-150232/.

Full text
Abstract:
Este trabalho apresenta uma alternativa ao controle vetorial de motores de indução, sem a utilização de sensores para realimentação da velocidade mecânica do motor. Ao longo do tempo, diversas técnicas de controle vetorial têm sido propostas na literatura. Dentre elas está a técnica de controle por orientação de campo (FOC), muito utilizada na indústria e presente também neste trabalho. A principal desvantagem do FOC é a sua grande sensibilidade às variações paramétricas da máquina, as quais podem invalidar o modelo e as ações de controle. Nesse sentido, uma estimativa correta dos parâmetros d
APA, Harvard, Vancouver, ISO, and other styles
6

Rodrigues, Marconi C?mara. "Identifica??o fuzzy-multimodelos para sistemas n?o lineares." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15143.

Full text
Abstract:
Made available in DSpace on 2014-12-17T14:54:55Z (GMT). No. of bitstreams: 1 MarconiCR_TESE.pdf: 2377871 bytes, checksum: c798a5eab76defef17ac0fe081e2453d (MD5) Previous issue date: 2010-03-16<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments ar
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
10

Spacca, Jordy Luiz Cerminaro. "Usando o Sistema de Inferência Neuro Fuzzy - ANFIS para o cálculo da cinemática inversa de um manipulador de 5 DOF /." Ilha Solteira, 2019. http://hdl.handle.net/11449/183448.

Full text
Abstract:
Orientador: Suely Cunha Amaro Mantovani<br>Resumo: No estudo dos manipuladores são utilizados os conceitos da cinemática direta e a inversa. No cálculo da cinemática direta tem-se a facilidade da notação de Denavit-Hartenberg, mas o desafio maior é a resolução da cinemática inversa, que se torna mais complexa conforme aumentam os graus de liberdade do manipulador, além de apresentar múltiplas soluções. As variáveis angulares obtidas pelas equações da cinemática inversa são utilizadas pelo controlador, para posicionar o órgão terminal do manipulador em um ponto específico de seu volume de traba
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "NEURO-FUZZY ANFIS"

1

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "NEURO-FUZZY ANFIS"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Igamberdiev, H. Z., A. N. Yusupbekov, U. F. Mamirov, and Sh D. Tulyaganov. "Regular Identification Algorithms for a Special Class of Neuro-Fuzzy Models ANFIS." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25252-5_97.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "NEURO-FUZZY ANFIS"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
7

Mota, Vania, and Daniel Leite. "Sistema de Inferência Neuro-Fuzzy para Análise Microbiológica de Processos de Compostagem." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1435.

Full text
Abstract:
A cama de galpões de confinamento de bovino leiteiro no modelo compost barn tem grande impacto na qualidade e produtividade animal. O objetivo deste estudo é desenvolver um modelo não-linear para estimar a quantidade de bactéria em camas de compostagem. A partir de variáveis de fácil mensuração, estimativas podem ser geradas pelo modelo e, consequentemente, análises laboratoriais caras e demoradas são evitadas. Um modelo de inferência neuro-fuzzy, ANFIS, é considerado. A pesquisa foi realizada em uma propriedade em Três Corações. Diferentes funções de pertinência fuzzy e algoritmos de aprendiz
APA, Harvard, Vancouver, ISO, and other styles
8

Bettocchi, R., M. Pinelli, P. R. Spina, and M. Venturini. "Artificial Intelligence for the Diagnostics of Gas Turbines: Part II — Neuro-Fuzzy Approach." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68027.

Full text
Abstract:
In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the set up of Neural Network (NN) models was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a Cycle Program, calibrated on a 255 MW single shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, su
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
&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
APA, Harvard, Vancouver, ISO, and other styles
10

Mohd. Hashim, S. Z., and M. O. Tokhi. "ANFIS Active Vibration Control of Flexible Beam Structures." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58204.

Full text
Abstract:
This paper presents the development of an adaptive neuro-fuzzy inference system (ANFIS) controller for vibration control of flexible beam structures. ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using the backpropagation algorithm and least squares method. This allows the fuzzy system to learn from the data modeling. To allow the non-linear dynamics of the system be incorporated within the design, a pseudo random binary signal (PRBS) covering the dynamic range of interest of the system is used to train the ANFIS model, which gives go
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'NEURO-FUZZY ANFIS' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography