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

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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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.

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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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Yeom, Chan-Uk, and Keun-Chang Kwak. "Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach." Applied Sciences 10, no. 23 (2020): 8495. http://dx.doi.org/10.3390/app10238495.

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We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, m

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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.

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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

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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.

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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

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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.

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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

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

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<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>

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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptatio

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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Minh, Tran Nhat, Nguyen Thanh Truyen, and Dinh Thi Hong Loan. "Artificial Neural Networks for Modeling Pollutant Removal in Wastewater Treatment: A Review." Galore International Journal of Applied Sciences and Humanities 8, no. 2 (2024): 88–98. http://dx.doi.org/10.52403/gijash.20240211.

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Water pollution poses global challenges to environmental sustainability and public health, necessitating effective wastewater treatment strategies. Traditional linear models often fail to capture the complexities of pollutant removal processes. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have emerged as powerful tools for modeling and optimizing wastewater treatment. ANNs excel in learning complex patterns and nonlinear relationships, while ANFIS integrates neural network learning with fuzzy logic to handle uncertainties in environmental systems. Case s

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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.

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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

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Olayode, Isaac Oyeyemi, Lagouge Kwanda Tartibu, and Frimpong Justice Alex. "Comparative Study Analysis of ANFIS and ANFIS-GA Models on Flow of Vehicles at Road Intersections." Applied Sciences 13, no. 2 (2023): 744. http://dx.doi.org/10.3390/app13020744.

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In the last two decades the efficient traffic-flow prediction of vehicles has been significant in curbing traffic congestions at freeways and road intersections and it is among the many advantages of applying intelligent transportation systems in road intersections. However, transportation researchers have not focused on prediction of vehicular traffic flow at road intersections using hybrid algorithms such as adaptive neuro-fuzzy inference systems optimized by genetic algorithms. In this research, we propose two models, namely the adaptive neuro-fuzzy inference system (ANFIS) and the adaptive

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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.

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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

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Zhang, X. Y., and B. Wei. "A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1207–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1207-2020.

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Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of A

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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.

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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

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Hosseinzadeh, Salaheddin, Hadi Larijani, Krystyna Curtis, and Andrew Wixted. "An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN." Applied System Innovation 2, no. 1 (2019): 10. http://dx.doi.org/10.3390/asi2010010.

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This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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Prajapati, Girraj, Kapil Parikh, and Prakash Baharni. "COMPARATIVE PERFORMANCE EVALUATION OF PSO-TUNED ANFIS MPPT ALGORITHM & GA-TUNED ANFIS MPPT ALGORITHM FOR SOLAR ENERGY CONVERSION SYSTEM." International Journal of Technical Research & Science 9, Spl (2024): 27–35. http://dx.doi.org/10.30780/specialissue-iset-2024/050.

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This paper introduces the development and performance evaluation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller designed for a DC-to-DC converter. The proposed system comprises a 2.0 kW PV array, a DC-to-DC boost converter, and a load. Leveraging the advantages of both neural and fuzzy networks, the proposed algorithm aims to optimize solar photovoltaic system parameters through the integration of an adaptive neuro-fuzzy inference system (ANFIS) controller, employing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The comparison between PSO and GA-based

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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Kumar, Ajit, and Ajoy Kanti Ghosh. "ANFIS-Delta method for aerodynamic parameter estimation using flight data." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 8 (2018): 3016–32. http://dx.doi.org/10.1177/0954410018791621.

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In this paper, aerodynamic parameters have been estimated using neuro-fuzzy-based novel approach (ANFIS-Delta). ANFIS-Delta is an extension of a feed-forward neural network based Delta method. This method augments the philosophies of an adaptive neuro-fuzzy inference system (ANFIS) in the Delta method. The current work studies the comparison of ANFIS-Delta estimated results with the existing methods using the flight data gathered on the Hansa-3 research aircraft at IIT Kanpur and also, demonstrates the efficacy of the algorithm on DLR HFB-320 aircraft data. Further, the robustness of the ANFIS

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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.

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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

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Prado, José Willer do, Caio Peixoto Chain, Mírian Rosa, and Alyce Cardoso Campos. "Tomada de decisão no mercado financeiro:." REVISTA ENIAC PESQUISA 13, no. 1 (2024): 95–119. http://dx.doi.org/10.22567/rep.v13i1.952.

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O presente trabalho tem por objetivo realizar a previsão das séries temporais do Índice Bovespa (Ibovespa) utilizando os métodos de Redes Neurais Artificiais – RNA e Adaptive Neuro Fuzzy Inference Systems – ANFIS, tendo em vista a busca de alternativas a modelos lineares que podem ignorar certos aspectos das estruturas dinâmicas existentes no mercado de ações. A metodologia foi de caráter descritiva e quantitativa. Para as técnicas de análise de dados, tendo em vista as possíveis características das séries temporais financeiras, optou-se por utilizar dois métodos não lineares para a análise, o

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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.

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<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

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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.

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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.

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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

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Chopra, Shivali, Gaurav Dhiman, Ashutosh Sharma, Mohammad Shabaz, Pratyush Shukla, and Mohit Arora. "Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences." Computational Intelligence and Neuroscience 2021 (September 3, 2021): 1–14. http://dx.doi.org/10.1155/2021/6455592.

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Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and sel

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A-Matarneh, Feras Mohammed, Bassam A. Y. Alqaralleh, Fahad Aldhaban, et al. "Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks." Computational Intelligence and Neuroscience 2022 (May 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/7940895.

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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such

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Prasetyo, Aldo Tri, Endryansyah ., Bambang Suprianto, and Puput Wanarti Rusimamto. "Desain Sistem Pengaturan Sudut Aero Pendulum Menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS) Berbasis MATLAB." JURNAL TEKNIK ELEKTRO 10, no. 2 (2021): 387–95. https://doi.org/10.26740/jte.v10n2.p387-395.

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Abstrak Aero pendulum 2dapat 2diartikan 2sebagai pendulum 2yang dilengkapi dengan baling baling di salah satu ujungnya, dan ujung 2lainnya berada pada 2satu titik 2tetap. Aero Pendulum terdiri dari dua posisi ekuilibrium (kesetimbangan) yaitu stabil dan tidak stabil. Salah satu masalah paling sederhana dalam robotika adalah masalah pengendalian posisi sudut. Penelitian ini bertujuan untuk merancang sistem kontrol Aero Pendulum dengan kendali Adaptive Neuro Fuzzy Inference System (ANFIS) yang disimulasikan di perangkat lunak Matlab. Kendali ANFIS telah diterapkan untuk mengontrol suatu sistem d

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Kusumah, Raden Muhamad Yuda Pradana, Maman Abdurohman, and Aji Gautama Putrada. "Basement Flood Control with Adaptive Neuro Fuzzy Inference System Using Ultrasonic Sensor." International Journal on Information and Communication Technology (IJoICT) 5, no. 2 (2020): 11. http://dx.doi.org/10.21108/ijoict.2019.52.482.

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This paper proposes a basement flood management system based on Adaptive Neuro Fuzzy Inference System (ANFIS). Basement is one of the main parts of a building that has a high potential for flooding. Therefore, the existence of a flood control system in the basement can be a solution to this threat. Water level control is the key to solving the problem. Fuzzy Inference System (FIS) has proven to be a reliable method in the control system but this method has limitations, that is, it needs to have a basis or a reference when determining the fuzzy set. When there is no basis or reference, Adaptive

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Alibak, Ali Hosin, Seyed Mehdi Alizadeh, Shaghayegh Davodi Monjezi, As’ad Alizadeh, Falah Alobaid, and Babak Aghel. "Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite." Membranes 12, no. 11 (2022): 1147. http://dx.doi.org/10.3390/membranes12111147.

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This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO2) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO2 permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the b

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Ikram, Rana Muhammad Adnan, Xinyi Cao, Tayeb Sadeghifar, Alban Kuriqi, Ozgur Kisi, and Shamsuddin Shahid. "Improving Significant Wave Height Prediction Using a Neuro-Fuzzy Approach and Marine Predators Algorithm." Journal of Marine Science and Engineering 11, no. 6 (2023): 1163. http://dx.doi.org/10.3390/jmse11061163.

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This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate

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