Log in

Relevant bibliographies by topics / ANFS- adaptive neuro-fuzzy system / Journal articles

To see the other types of publications on this topic, follow the link: ANFS- adaptive neuro-fuzzy system.

Journal articles on the topic 'ANFS- adaptive neuro-fuzzy system'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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

Select a source type:

Consult the top 50 journal articles for your research on the topic 'ANFS- adaptive neuro-fuzzy system.'

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.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Senivarapu Ankit Reddy and Dr. Vustelamuri Padmavathi. "Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 5 (2024): 196–200. http://dx.doi.org/10.32628/ijsrset24115113.

Full text

Abstract:

Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discus

APA, Harvard, Vancouver, ISO, and other styles

2

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

3

Vladov, Serhii, Maryna Bulakh, Victoria Vysotska, and Ruslan Yakovliev. "Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System." Energies 17, no. 16 (2024): 4195. http://dx.doi.org/10.3390/en17164195.

Full text

Abstract:

A modified onboard neuro-fuzzy adaptive (NFA) helicopter turboshaft engine (HTE) automatic control system (ACS) is proposed, which is based on a circuit consisting of a research object, a regulator, an emulator, a compensator, and an observer unit. In this scheme, it is proposed to use the proposed AFNN six-layer hybrid neuro-fuzzy network (NFN) with Sugeno fuzzy inference and a Gaussian membership function for fuzzy variables, which makes it possible to reduce the HTE fuel consumption parameter transient process regulation time by 15.0 times compared with the use of a traditional system autom

APA, Harvard, Vancouver, ISO, and other styles

4

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

5

Barhoum, Tarek. "COMPARATIVE STUDY BETWEEN EXTENDED ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (EANFIS) AND CO-ACTIVE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (CANFIS) IN CONTROLLING FULL CAR ACTIVE SUSPENSION SYSTEM." INTERNATIONAL JOURNAL OF CURRENT RESEARCH 8, no. 0975-833X (2016): 36921–30. https://doi.org/10.5281/zenodo.15353284.

Full text

Abstract:

This Paper presents two adaptive vehicle suspension control methods, which significantly improvethe performance of mechatronic suspension systems in full car model by absorbing shocks caused bybumpy roads and preventing vibrations from reaching the cockpit and providing stability andcoherence required. The first control approach is an extension to the Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) called Extended adaptive Neuro fuzzy inference system (EANFIS). The secondcontrol approach is a special type of multi-inputs multi-outputs ANFIS model called Co-Activeadaptive Neuro fuzzy inference sys

APA, Harvard, Vancouver, ISO, and other styles

6

Alić, Senad, Safet Brdarević, Sabahudin Jašarević, and Mustafa Imamović. "INTRODUCTION OF EXSPERT SYSTEM ANFIS INTO MAINTENANCE SYSTEM OF PROCESS FANS." Mašinstvo 12, no. 2 (2015): 41–51. https://doi.org/10.62456/jmem.2015.02.041.

Full text

Abstract:

&lt;p style=&quot;text-align: justify;&quot;&gt;Neuro-fuzzy systems represent a modern class of hybrid intelligent systems. They are described as artificial neural networks characterized by fuzzy parameters. The combination of two different concepts of artificial intelligence tries to take of individual advantages of fuzzy logic and artificial neural networks in hybrid systems of homogeneous structure. Such systems are increasingly being used for solving of everyday complex problems. The possibility to display fuzzy models in the form of neural network is commonly used in the p

APA, Harvard, Vancouver, ISO, and other styles

7

Jamali, A., H. Babaei, N. Nariman-Zadeh, SH Ashraf Talesh, and T. Mirzababaie Mostofi. "Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 234, no. 3 (2016): 368–78. http://dx.doi.org/10.1177/1464420716660332.

Full text

Abstract:

Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function’s variables a

APA, Harvard, Vancouver, ISO, and other styles

8

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

9

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

10

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

11

Timene, Aristide, Ndjiya Ngasop, and Haman Djalo. "Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." Proceedings of Engineering and Technology Innovation 19 (May 25, 2021): 53–61. http://dx.doi.org/10.46604/peti.2021.7522.

Full text

Abstract:

This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations. Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement. The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, a

APA, Harvard, Vancouver, ISO, and other styles

12

Haviluddin, Haviluddin, Herman Santoso Pakpahan, Novianti Puspitasari, Gubtha Mahendra Putra, Rima Yustika Hasnida, and Rayner Alfred. "Adaptive Neuro-Fuzzy Inference System for Waste Prediction." Knowledge Engineering and Data Science 5, no. 2 (2022): 122. http://dx.doi.org/10.17977/um018v5i22022p122-128.

Full text

Abstract:

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test re

APA, Harvard, Vancouver, ISO, and other styles

13

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

14

Jayasri, S. K., and V. Poongodi. "Adaptive Neuro-Fuzzy Inference System Based Impulse Denoising." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1847–51. http://dx.doi.org/10.1166/jctn.2020.8452.

Full text

Abstract:

Removing impulse noise from images is a critical issue in image processing because it may occur frequently during acquisition or transmission of images. We propose an anfis based impulse denoising algorithm to preserve the intrinsic geometric details of an image. The main target of this project is to restore the features of an image without losing any information from the degraded image. This method is more suitable to preserve the features of an image with scale invariant properties of an image. Here we are performing the training the noisy image with ANFIS and testing the image to retain the

APA, Harvard, Vancouver, ISO, and other styles

15

Harkouss, Youssef. "Accurate modeling and optimization of microwave circuits and devices using adaptive neuro-fuzzy inference system." International Journal of Microwave and Wireless Technologies 3, no. 6 (2011): 637–45. http://dx.doi.org/10.1017/s1759078711000651.

Full text

Abstract:

In this paper, an accurate neuro-fuzzy-based model is proposed for efficient computer-aided design (CAD) modeling and optimization of microwave circuits and devices. The adaptive neuro-fuzzy inference system (ANFIS) approach is used to determine the scattering parameters of a microstrip filter and is applied to the optimization design of this microstrip filter. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of artificial neural networks. The neuro-fuzzy model has been trained and tested with different sets of input/output data. Finally, diffe

APA, Harvard, Vancouver, ISO, and other styles

16

Walia, Navneet, Harsukhpreet Singh, and Anurag Sharma. "ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey." International Journal of Computer Applications 123, no. 13 (2015): 32–38. http://dx.doi.org/10.5120/ijca2015905635.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

18

Luo, Yunhui, Xingguang Wang, Qing Wang, and Yehong Chen. "Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System." Applied Sciences 11, no. 21 (2021): 9936. http://dx.doi.org/10.3390/app11219936.

Full text

Abstract:

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this

APA, Harvard, Vancouver, ISO, and other styles

19

Khalil, Ahmed S., Sergey V. Starovoytov, and Nikolai S. Serpokrylov. "The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution Predicting by Biochar." Materials Science Forum 931 (September 2018): 985–90. http://dx.doi.org/10.4028/www.scientific.net/msf.931.985.

Full text

Abstract:

The adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the removal of ammonium () from wastewater. The ANFIS model was developed and validated with a data set from a pilot-scale of adsorption system treating aqueous solutions and wastewater from fish farms. The data sets consist of four parameters, which include pH, temperature, an initial concentration of ammonium and amount of adsorbent. The adsorbent was biochar obtained from rice straw. The ANFIS models performance was assessed through the root mean absolute error (RMSE) and was validated by testing data. The resu

APA, Harvard, Vancouver, ISO, and other styles

20

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

21

Harahap, Sanggam Andreas, and Sukmawati Nur Endah. "Penerapan Adaptive Neuro-Fuzzy Inference System untuk Prediksi Nilai Tukar Rupiah." JURNAL MASYARAKAT INFORMATIKA 10, no. 1 (2019): 37–47. http://dx.doi.org/10.14710/jmasif.10.1.31488.

Full text

Abstract:

Salah satu indikator penting dalam perekonomian suatu negara adalah nilai tukar dari mata uang, dimana majunya suatu negara dapat ditentukan oleh kekuatan nilai mata uang negara tersebut. Nilai tukar yang berdasarkan pada kekuatan pasar akan selalu berubah disetiap kali nilai-nilai salah satu dari dua komponen mata uang berubah. Dengan mampu meramalkan perubahan nilai tukar mata uang tersebut maka dapat ditentukan harga yang tepat untuk menukarkan mata uang para pemilik modal ke dalam bentuk mata uang lain. Proses peramalan/ prediksi dapat dilakukan dengan menggunakan arsitektur jaringan adapt

APA, Harvard, Vancouver, ISO, and other styles

22

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. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp293-299.

Full text

Abstract:

<p>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.</p>

APA, Harvard, Vancouver, ISO, and other styles

23

Angga debby frayudha, Aris Yulianto, and Fatmawatul Qomariyah. "PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)." Explore IT! : Jurnal Keilmuan dan Aplikasi Teknik Informatika 12, no. 1 (2020): 6–17. http://dx.doi.org/10.35891/explorit.v12i1.2020.

Full text

Abstract:

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan b

APA, Harvard, Vancouver, ISO, and other styles

24

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

25

Intidam, Abdessamad, Hassan El Fadil, Halima Housny, et al. "Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles." Energies 16, no. 11 (2023): 4395. http://dx.doi.org/10.3390/en16114395.

Full text

Abstract:

This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter

APA, Harvard, Vancouver, ISO, and other styles

26

Ikram, Misbah, Hongbo Liu, Ahmed Mohammed Sami Al-Janabi, et al. "Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm." Water 16, no. 21 (2024): 3038. http://dx.doi.org/10.3390/w16213038.

Full text

Abstract:

For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMS

APA, Harvard, Vancouver, ISO, and other styles

27

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

28

Phetchanchai, Chawalsak, Chuthawuth Chantaramalee, Napatsarun Chatchawalanont, and Piyapong Phatcha. "Forecasting East Asian Tourist Arrivals to Thailand with Adaptive Neuro-Fuzzy Inference System." Global Journal of Engineering and Technology Review Vol.4 (1) January-March. 2019 4, no. 1 (2019): 1–8. http://dx.doi.org/10.35609/gjetr.2019.4.1(1).

Full text

Abstract:

Objective - This research aims to propose the approach of forecasting tourist arrivals to Thailand. Methodology/Technique – Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as our forecasting method by using fuzzy C-means clustering as a technique for the partitioning training dataset Findings - The appropriate parameter of time lag was found for each dataset of East Asian tourist arrivals to Thailand. Novelty - The forecasting procedure with the appropriate parameter of time lag was represented our work as a novelty idea. Type of Paper: Empirical. Keywords: Tourist arrivals forecasting,

APA, Harvard, Vancouver, ISO, and other styles

29

Nurul, Najihah Che Razali, Ab. Ghani Ngahzaifa, Izhar Hisham Syifak, Kasim Shahreen, Satya Widodo Nuryono, and Sutikno Tole. "Rainfall-runoff modelling using adaptive neuro-fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 1117–26. https://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126.

Full text

Abstract:

This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m3/s). For model parameters, the models apply three triangle membership functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square Error (RMSE). Models with good performance in tr

APA, Harvard, Vancouver, ISO, and other styles

30

Ardana, Dedy, Irwandi Irwandi, Umar Muksin, and Mochammad Vicky Idris. "Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System Method." Jurnal Penelitian Pendidikan IPA 11, no. 2 (2025): 593–601. https://doi.org/10.29303/jppipa.v11i2.10148.

Full text

Abstract:

This study analyzes rainfall prediction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method to improve model accuracy, particularly in extreme rainfall events. The objective of this study is to evaluate rainfall prediction using the ANFIS method to enhance model accuracy, especially in predicting extreme rainfall occurrences. The results indicate a moderate positive correlation with R² of 0.55, demonstrating good model performance at low rainfall levels (<20 mm) but a tendency to underestimate high-intensity rainfall (>60 mm). Residual analysis reveals a distribution around ze

APA, Harvard, Vancouver, ISO, and other styles

31

Sharma, Durgesh, Suresh Kumar Garg, and Chitra Sharma. "Neuro Fuzzy Studies of Effect of Flexibilities on Performance of Flexible Manufacturing System." Advanced Materials Research 622-623 (December 2012): 56–59. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.56.

Full text

Abstract:

The paper presents a Neuro-fuzzy study of Flexible Manufacturing System subject to different design and control strategies. Adaptive Neuro-Fuzzy inference system (ANFIS) techniques have been used to evaluate the performance. The objective of our work is to evaluating the performances of system in terms of Make Span Time at different levels of Routing and Machine flexibilities.

APA, Harvard, Vancouver, ISO, and other styles

32

Che Razali, Nurul Najihah, Ngahzaifa Ab Ghani, Syifak Izhar Hisham, Shahreen Kasim, Nuryono Satya Widodo, and Tole Sutikno. "Rainfall-runoff modelling using adaptive neuro-fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (2020): 1117. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp1117-1126.

Full text

Abstract:

<span lang="EN-GB">This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m<sup>3</sup>/s). For model parameters, the models apply three triangle membership functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square

APA, Harvard, Vancouver, ISO, and other styles

33

Al-Mekhlafi, Mohammed A. A., Herman Wahid, and Azian Abd Aziz. "Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3657. http://dx.doi.org/10.11591/ijece.v8i5.pp3657-3665.

Full text

Abstract:

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulatio

APA, Harvard, Vancouver, ISO, and other styles

34

Mohammed, A. A. Al-Mekhlafi, Wahid Herman, and Abd Aziz Azian. "Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3657–65. https://doi.org/10.11591/ijece.v8i5.pp3657-3665.

Full text

Abstract:

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulatio

APA, Harvard, Vancouver, ISO, and other styles

35

FAKHRUDDIN, HANIF HASYIER, HANDRI TOAR, ERA PURWANTO, et al. "Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 9, no. 4 (2021): 786. http://dx.doi.org/10.26760/elkomika.v9i4.786.

Full text

Abstract:

ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membukti

APA, Harvard, Vancouver, ISO, and other styles

36

Nazerian, Morteza, Fateme Naderi, Ali Partovinia, Antonios N. Papadopoulos, and Hamed Younesi-Kordkheili. "Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 236, no. 1 (2021): 3–22. http://dx.doi.org/10.1177/14644207211024278.

Full text

Abstract:

The aim of this paper was to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) and to predict the flexural strength of the sandwich panels made with thin medium density fiberboard as surface layers, and polyurethane foam as a core layer, by applying metaheuristic optimization methods. For this purpose, various models, namely ant colony optimization for the continuous domain (ACOR), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) were applied and compared, as different efficient bio-inspired paradigms, to assess their suitabi

APA, Harvard, Vancouver, ISO, and other styles

37

Gopu, P., M. Dev Anand, and . "RSM And ANFIS Based Parameters Prediction of Robot Using GRA." International Journal of Engineering & Technology 7, no. 4.36 (2018): 604. http://dx.doi.org/10.14419/ijet.v7i4.36.24208.

Full text

Abstract:

Ability of robot arm manipulation must be highly accurate and repeatable one. Performance uncertainty is causes by some noise factor. The effects of these factors were model to reduce the uncertainty of the robotic arm performance. In this paper highlights the prediction of output parameters robot cell data like X, Y and Z axis through Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) for reduce the performance variation of the robot. The input kinematic parameters like θ1, θ2, θ3, θ4, θ5 has been considered and the output multi objective parameters X, Y and

APA, Harvard, Vancouver, ISO, and other styles

38

Ramesh, K., A. P. Kesarkar, J. Bhate, M. Venkat Ratnam, and A. Jayaraman. "Adaptive neuro fuzzy inference system for profiling of the atmosphere." Atmospheric Measurement Techniques Discussions 7, no. 3 (2014): 2715–36. http://dx.doi.org/10.5194/amtd-7-2715-2014.

Full text

Abstract:

Abstract. Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropi

APA, Harvard, Vancouver, ISO, and other styles

39

Krichevsky, Mikhail, Artyr Bydagov, and Julia Martynova. "Assessment of the efficiency of educational project management using neuro-fuzzy system." E3S Web of Conferences 110 (2019): 02070. http://dx.doi.org/10.1051/e3sconf/201911002070.

Full text

Abstract:

The project represents the introduction of elements and methods of artificial intelligence in the work programs of disciplines in the direction of “Management”. To assess the efficiency of such project management, it was proposed to use tools related to machine learning methods that include neural networks and fuzzy logic. The results of such an assessment are obtained using a neuro-fuzzy anfis (adaptive neuro-fuzzy inference system) type system, which is implemented using the MATLAB R2018b software package.

APA, Harvard, Vancouver, ISO, and other styles

40

Singh, Sunil Kumar, and Raj Shree. "Smart Prediction Method of Software Defect Using Neuro-Fuzzy Approach." Asian Journal of Computer Science and Technology 7, no. 2 (2018): 6–10. http://dx.doi.org/10.51983/ajcst-2018.7.2.1878.

Full text

Abstract:

Faults in software program structures continue to be a primary problem. A software fault is a disorder that reasons software failure in an executable product. A form of software fault predictions techniques were proposed, however none has proven to be continually correct. So, on this examine the overall performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting software program defects and software program reliability has been reviewed. The datasets are taken from NASA Metrics Data Program (MDP) statistics repository. In the existing work a synthetic intelligence technique

APA, Harvard, Vancouver, ISO, and other styles

41

Pérez-Pérez, Esvan-Jesús, Yair González-Baldizón, José-Armando Fragoso-Mandujano, Julio-Alberto Guzmán-Rabasa, and Ildeberto Santos-Ruiz. "Data-Driven Fault Diagnosis in Water Pipelines Based on Neuro-Fuzzy Zonotopic Kalman Filters." Mathematical and Computational Applications 30, no. 1 (2024): 2. https://doi.org/10.3390/mca30010002.

Full text

Abstract:

This work presents a data-driven approach for diagnosing sensor faults and leaks in hydraulic pipelines using neuro-fuzzy Zonotopic Kalman Filters (ZKF). The approach involves two key steps: first, identifying the nonlinear pipeline system using an adaptive neuro-fuzzy inference system (ANFIS), resulting in a set of Takagi–Sugeno fuzzy models derived from pressure and flow data, and second, implementing a neuro-fuzzy ZKF bench to detect pipeline leaks and sensor faults with adaptive thresholds. The learning phase of the neuro-fuzzy systems considers only fault-free data. Fault isolation is ach

APA, Harvard, Vancouver, ISO, and other styles

42

Petković, Dalibor, Milan Gocić, and Shahaboddin Shamshirband. "ADAPTIVE NEURO-FUZZY COMPUTING TECHNIQUE FOR PRECIPITATION ESTIMATION." Facta Universitatis, Series: Mechanical Engineering 14, no. 2 (2016): 209. http://dx.doi.org/10.22190/fume1602209p.

Full text

Abstract:

The paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia during 1946-2012 are used as case studies. Even though a number of mathematical functions have been proposed for modeling the precipitation estimation, these models still suffer from the disadvantages such as their being very demanding in terms of calculation time. Artificial neural network (ANN) can be used as an alternative to the analytical approach since it offers advantages such as no required knowledge of intern

APA, Harvard, Vancouver, ISO, and other styles

43

Parhi, D. R., and M. K. Singh. "Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 6 (2010): 1369–81. http://dx.doi.org/10.1243/09544062jmes1751.

Full text

Abstract:

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include t

APA, Harvard, Vancouver, ISO, and other styles

44

Patel, Dushyant, and Dr Falguni Parekh. "Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)." International Journal of Engineering Trends and Technology 12, no. 10 (2014): 510–14. http://dx.doi.org/10.14445/22315381/ijett-v12p295.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Shahbudin, Shahrani, Murizah Kassim, Roslina Mohamad, Saiful Izwan Suliman, and Yuslinda Wati Mohamad Yusof. "Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1196. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1196-1202.

Full text

Abstract:

This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classiﬁcation tasks using extracted features based on S-transforms methods. The ANFIS model with a ﬁve-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal. Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that

APA, Harvard, Vancouver, ISO, and other styles

46

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

47

Sinaga, Chrisman Bonor, Haviluddin Haviluddin, Herman Santoso Pakpahan, Anton Prafanto, and Hario Jati Setyadi. "Peramalan Curah Hujan Dengan Pendekatan Adaptive Neuro Fuzzy Inference System." Sains, Aplikasi, Komputasi dan Teknologi Informasi 1, no. 2 (2019): 1. http://dx.doi.org/10.30872/jsakti.v1i2.2599.

Full text

Abstract:

Analisa peramalan curah hujan yang mendekati kenyataan berdasarkan akurasi yang akurat sangat diperlukan dalam berbagai aktivitas kehidupan manusia. Paper ini bertujuan untuk menerapkan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam peramalan curah hujan di Kota Samarinda, Kalimantan Timur. Beberapa parameter ANFIS seperti MF (Fungsi Keanggotaan), Input MF type (Tipe Fungsi Keanggotaan), Learning Rate (Step Size), dan rasio data telah digunakan. Berdasarkan hasil percobaan akurasi peramalan yang diperoleh cukup akurat dengan nilai MSE adalah 0.063290962 untuk rasio data 3:2 dan ada

APA, Harvard, Vancouver, ISO, and other styles

48

Kusanti, Jani, and Sri Hartati. "Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, no. 2 (2015): 187. http://dx.doi.org/10.22146/ijccs.7547.

Full text

Abstract:

AbstrakPenggunaan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam proses identifikasi salah satu gangguan neurologis pada bagian kepala yang dikenal dalam istilah kedokteran stroke ischemic dari hasil ct scan kepala dengan tujuan untuk mengidentifikasi lokasi yang terkena stroke ischemik. Langkah-langkah yang dilakukan dalam proses identifikasi antara lain ekstraksi citra hasil ct scan kepala dengan menggunakan histogram. Citra hasil proses histogram ditingkatkan intensitas hasil citranya dengan menggunakan threshold otsu sehingga didapatkan hasil pixel yang diberi nilai 1 berkaitan

APA, Harvard, Vancouver, ISO, and other styles

49

Amini, Reza, and S. C. Ng. "Comparison of Artificial Neural Network, Fuzzy Logic and Adaptive Neuro-Fuzzy Inference System on Air Pollution Prediction." Journal of Engineering & Technological Advances 2, no. 1 (2017): 14–22. http://dx.doi.org/10.35934/segi.v2i1.14.

Full text

Abstract:

Air pollution can have major impacts on living being and society. Different systems has been developed to predict upcoming air pollution. These prediction systems use different types of models for predicting the air pollution. This paper aims to compare the popular models being used to predict air pollution. The significant models are Artificial Neural Network (ANN), Fuzzy Logic and Adaptive Neuro- Fuzzy Inference System (ANFIS). These models are not only being applied in air pollution prediction, but they were applied in different fields such as fuel consumption and pattern recognition. The s

APA, Harvard, Vancouver, ISO, and other styles

50

P, Sobha Rani, Padma R, Sarveswara Prasad R, and Rathnakar Kumar P. "Enhancement of power quality in grid connected PV system." Indian Journal of Science and Technology 13, no. 35 (2020): 3630–41. https://doi.org/10.17485/IJST/v13i35.1266.

Full text

Abstract:

Abstract <strong>Background/Objectives:</strong> In grid connected photo voltaic systems inverter is the key element. The inverter is required to shape dc current into sinusoidal current and provide fast response under various disturbances. The quality of power injected into the grid depends on proper inverter control. The objective of this paper is reducing harmonics and to improve power factor in grid connected system with balanced and unbalanced loads.<strong>Methods/Statistical analysis:</strong> In this study, three control mechanisms, adaptive neuro fuzzy inference system (ANFI

APA, Harvard, Vancouver, ISO, and other styles

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!