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Journal articles on the topic 'NEURO-FUZZY ANFIS'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 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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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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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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Sadeghi-Niaraki, Abolghasem, Ozgur Kisi, and Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods." PeerJ 8 (August 14, 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.

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

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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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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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9

Sabet, Masumeh, Mehdi Naseri, and Hosein Sabet. "Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System." Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 42, no. 1 (2010): 159–67. http://dx.doi.org/10.2478/v10060-008-0074-6.

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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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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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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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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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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): 293–99. 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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16

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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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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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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Abdallah, El, Maamar Laidi, and Salah Hanini. "New method based on neuro-fuzzy system and PSO algorithm for estimating phase equilibria properties." Chemical Industry and Chemical Engineering Quarterly, no. 00 (2021): 24. http://dx.doi.org/10.2298/ciceq201104024a.

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The subject of this work is to propose a new method based on ANFIS system and PSO algorithm to conceive a model for estimating the solubility of solid drugs in sc-CO2. The high nonlinear process was modeled by neuro-fuzzy approach (NFS). The PSO algorithm was used for two purposes: replacing the standard back propagation in training the NFS and optimizing the process. The validation strategy have been carried out using a linear regression analysis of the predicted versus experimental outputs. The ANFIS approach is compared to the ANN in terms of accuracy. Statistical analysis of the predictabi

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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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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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Abdelazziz, Aouiche, Aouiche El Moundher, and Guiza Dhaouadi. "Efficient Neuro-Fuzzy Identification Model for Electrocardiogram Signal." Journal Européen des Systèmes Automatisés 55, no. 2 (2022): 237–44. http://dx.doi.org/10.18280/jesa.550211.

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This paper addresses the performance of the Artificial Neural Networks (ANNs), Fuzzy inference systems (FISs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for the identification of some nonlinear systems with certain degree of uncertainty. The efficiency of the suggested methods in modeling and identification the responses were analyzed and compared. The Back-propagation algorithm and Takagi-Sugeno (TS) approach are used to train the ANNs, FISs and ANFIS, respectively. In this study we will show how ANFIS can be put in order to form nets that able to train from external data and informati

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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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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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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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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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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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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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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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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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Badrzadeh, Honey, Ranjan Sarukkalige, and A. W. Jayawardena. "Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model." Hydrology Research 49, no. 1 (2017): 27–40. http://dx.doi.org/10.2166/nh.2017.163.

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Abstract In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using th

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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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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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Djelamda, Imene, and Ilhem Bochareb. "Field-oriented control based on adaptive neuro-fuzzy inference system for PMSM dedicated to electric vehicle." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1892–901. http://dx.doi.org/10.11591/eei.v11i4.3818.

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Permanent magnet synchronous motor (PMSM) speed control is generally done using flux-oriented control, which uses conventional proportional-integral (PI) current regulators, but still remain the problem of calculating the coefficients of these regulators, particularly in the case of control hybridization, the development of artificial intelligence has simplified many calculations while giving more accurate, and improved results, this paper presents and compares the performance of the flux oriented control (FOC) of a PMSM powered by pulse width modulation (PWM) using PI regulator, fuzzy logic c

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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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NG, GEOK SEE, SEVKI ERDOGAN, DAMING SHI, and ABDUL WAHAB. "INSIGHT OF FUZZY NEURAL SYSTEMS IN THE APPLICATION OF HANDWRITTEN DIGITS CLASSIFICATION." International Journal of Image and Graphics 06, no. 04 (2006): 511–32. http://dx.doi.org/10.1142/s0219467806002410.

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There have been many applications in the area of handwritten character recognition. Over the last decade much research has gone into developing algorithms to accurately convert captured images of handwriting to text. At the same time, research into neuro fuzzy classification models has proven to solve many complex problems. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) and Evolving Fuzzy Neural Network (EFuNN) was investigated and studied in detail on how these two models can be used to perform handwritten digits classification. Results of the experiments show great potential of

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Sahoby, LALAOHARISOA, VELO Jérôme, MANASINA Ruffin, RANDRIANANTENAINA Todihasina Roselin, and RATIARISON Adolphe Andriamanga. "Modeling The Results Of A Perceptron And Neuro-Fuzzy Neural Network Simulation (ANFIS)." International Journal of Progressive Sciences and Technologies 38, no. 1 (2023): 448. http://dx.doi.org/10.52155/ijpsat.v38.1.5250.

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The objective of this work is to model simulation data of a dust devils in Comsol using neuro-fuzzy methods (ANFIS: Adaptive Neuro Fuzzy Inference Systems) and perceptron neural networks. Since the number of simulations performed was insufficient, we used the Spline function to increase the amount of data. The results show that neuro-fuzzy is more effective than perceptron neural networks. The obtained models are excellent, with a Nash -Sutcliffe criterion value above 90%.

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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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Kheioon, Imad A., Raheem Al-Sabur, and Abdel-Nasser Sharkawy. "Design and Modeling of an Intelligent Robotic Gripper Using a Cam Mechanism with Position and Force Control Using an Adaptive Neuro-Fuzzy Computing Technique." Automation 6, no. 1 (2025): 4. https://doi.org/10.3390/automation6010004.

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Manufacturers increasingly turn to robotic gripper designs to improve the efficiency of gripping and moving objects and provide greater flexibility to these objects. Neuro-fuzzy techniques are the most widespread in developing gripper designs. In this study, the traditional gripper design is modified by adding a suitable cam that makes it compatible with the basic design, and an adaptive neuro-fuzzy inference system (ANFIS) is used in a MATLAB Simulink environment. The developed gripper investigates the follower path concerning the cam surface curve, and the gripper position is controlled usin

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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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ZANCHETTIN, CLEBER, LEANDRO L. MINKU, and TERESA B. LUDERMIR. "DESIGN OF EXPERIMENTS IN NEURO-FUZZY SYSTEMS." International Journal of Computational Intelligence and Applications 09, no. 02 (2010): 137–52. http://dx.doi.org/10.1142/s1469026810002823.

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Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in recent years. These systems are robust solutions that search for representations of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and definition of the parameter effectiveness of such systems is still a hard task. In the present work, we perform a statistical analysis to verify interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis is carried out using a powerful statistical tool, namely,

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Papageorgiou, Konstantinos, Elpiniki I. Papageorgiou, Katarzyna Poczeta, Dionysis Bochtis, and George Stamoulis. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System." Energies 13, no. 9 (2020): 2317. http://dx.doi.org/10.3390/en13092317.

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(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy

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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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Ouifak, Hafsaa, Zaineb Afkhkhar, Alain Thierry Iliho Manzi, and Ali Idri. "Homogenous Ensembles of Neuro-Fuzzy Classifiers using Hyperparameter Tuning for Medical Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 03 (2024): 273–301. http://dx.doi.org/10.1142/s0218488524500119.

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Neuro-fuzzy techniques have been widely used in many applications due to their ability to generate interpretable fuzzy rules. Ensemble learning, on the other hand, is an emerging paradigm in artificial intelligence used to improve performance results by combining multiple single learners. This paper aims to develop and evaluate a set of homogeneous ensembles over four medical datasets using hyperparameter tuning of four neuro-fuzzy systems: adaptive neuro-fuzzy inference system (ANFIS), Dynamic evolving neuro-fuzzy system (DENFIS), Hybrid fuzzy inference system (HyFIS), and neuro-fuzzy classif

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Dosdoğru, Ayşe Tuğba. "Improving Weather Forecasting Using De-Noising with Maximal Overlap Discrete Wavelet Transform and GA Based Neuro-Fuzzy Controller." International Journal on Artificial Intelligence Tools 28, no. 03 (2019): 1950012. http://dx.doi.org/10.1142/s021821301950012x.

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Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is one of the most important neuro-fuzzy systems. ANFIS can be successfully applied to different real-world problems. However, it is difficult to create the ANFIS structure whose parameters directly influence the solutions. Therefore, hybrid ANFIS methods are generally used to increase efficiency and adaptability. This paper used an integrated neuro-fuzzy controller that is also known as PATSOS. The main purpose of this study is to improve the performance of the PATSOS method for weather forecasting. Our proposed PATSOS method is different from th

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Kharola, Ashwani, and Pravin P. Patil. "Stabilization and Control of Elastic Inverted Pendulum System (EIPS) Using Adaptive Fuzzy Inference Controllers." International Journal of Fuzzy System Applications 6, no. 4 (2017): 21–32. http://dx.doi.org/10.4018/ijfsa.2017100102.

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Elastic Inverted Pendulum system (EIP) are very popular objects of theoretical investigation and experimentation in field of control engineering. The system becomes highly nonlinear and complex due to transverse displacement of elastic pole or pendulum. This paper presents a comparison study for control of EIP using fuzzy and hybrid adaptive neuro fuzzy inference system (ANFIS) controllers. Initially a fuzzy controller was designed, which was used for training and tuning of ANFIS controller using gbell shape membership functions (MFs). The performance of complete system was evaluated through o

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Lassoued, Hela, Raouf Ketata, and Hajer Ben Mahmoud. "Optimal Neuro Fuzzy Classification for Arrhythmia Data Driven System." International Journal of Innovative Technology and Exploring Engineering 11, no. 1 (2021): 70–80. http://dx.doi.org/10.35940/ijitee.a9628.1111121.

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This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS con

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Hela, Lassoued, Ketata Raouf, and Ben Mahmoud Hajer. "Optimal Neuro-Fuzzy Classification for Arrhythmia Data Driven System." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 1 (2021): 70–80. https://doi.org/10.35940/ijitee.A9628.1111121.

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This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS con

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