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Journal articles on the topic 'Adaptive Neural Network Fuzzy Inference System'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Titov, Andrei P. "SOFTWARE IMPLEMENTATION OF THE CO-ACTIVE NEURO-FUZZY INFERENCE SYSTEM." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 2 (2024): 26–43. http://dx.doi.org/10.28995/2686-679x-2024-2-26-43.

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The article deals with the implementation of a neural network with fuzzy logic based on the Co-Active Neuro-Fuzzy Inference System (CANFIS) model. The CANFIS model is an adaptive neuro-fuzzy system that combines neural networks and fuzzy logic for processing data with uncertainty and fuzziness. CANFIS uses fuzzy rules and output mechanisms to convert input data into output values. It consists of several layers, including an input layer, hidden layers and an output layer, where each layer contains neurons performing fuzzy activation and output of results. The relevance of the work lies in the f

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Kotkova, E. "Neural network model for predicting pre-evacuation behavior of people in case of fire." National Security and Strategic Planning 2022, no. 2 (2022): 66–72. http://dx.doi.org/10.37468/2307-1400-2022-2-66-72.

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This article discusses a comprehensive study of pre-evacuation behavior of people in the event of an emergency. In this regard, it is advisable to use machine learning approaches, in particular neural networks, for data mining in the field of security. Statistical data obtained in emergency situations may be limited and generally uncertain, for this reason it is recommended to choose a neural network architecture - adaptive fuzzy inference Network System (ANFIS) based on the Takagi–Sugeno fuzzy inference system. The neural network architecture considered in the article in the form of an adapti

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Hu, Xingliu, Haifei Si, Hao Shen, and Zhenzhong Yu. "A fuzzy neural network model to determine axial strain measured by a long-period fiber grating sensor." Measurement and Control 53, no. 3-4 (2020): 704–10. http://dx.doi.org/10.1177/0020294019901307.

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The paper reports an adaptive-network-based fuzzy inference system for the measurement of axial strain using long-period fiber grating sensors. The long-period fiber grating sensor supports optical resonances, which are sensitive to the change of axial strain. The axial strain can be quantified based on the wavelength shift and amplitude changes of the optical resonance. To improve the accuracy of axial strain quantification, this paper proposes the adaptive-network-based fuzzy inference system model. The adaptive-network-based fuzzy inference system model is trained using the strain data meas

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CHAI, YUANYUAN, and LIMIN JIA. "CHOQUET INTEGRAL–OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION." International Journal of Computational Intelligence and Applications 10, no. 01 (2011): 15–34. http://dx.doi.org/10.1142/s1469026811002970.

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In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral–OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neura

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Asghar, Aamer Bilal, Saad Farooq, Muhammad Shahzad Khurram, Mujtaba Hussain Jaffery, and Krzysztof Ejsmont. "Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm." Energies 15, no. 1 (2021): 211. http://dx.doi.org/10.3390/en15010211.

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Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The

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Yu, Hao. "Inverted Pendulum System Modeling and Fuzzy Neural Networks Control." Applied Mechanics and Materials 268-270 (December 2012): 1371–75. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1371.

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Inverted pendulum on a cart poses a challenging control problem. It seems to have been one of attractive tools for testing linear and nonlinear control laws. In this paper, we adopt PID and the adaptive neural network based fuzzy inference method to control the inverted pendulum, combined the fuzzy control into the neural control. This method can improve the capability of the fuzzy controller through learning the data of PID controller to train the fuzzy controller. When the model parameters were changed, the adaptive neural network based fuzzy inference system had good adopt ability to anti-i

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Gao, Ming Ming, and Liang Shan. "The Study of System Model Based on Fuzzy Inference and Neural Network." Applied Mechanics and Materials 197 (September 2012): 547–52. http://dx.doi.org/10.4028/www.scientific.net/amm.197.547.

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For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. Thi

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Gennady, Kaniuk, Vasylets Tetiana, Varfolomiyev Oleksiy, Mezerya Andrey, and Antonenko Nataliia. "Development of neuralnetwork and fuzzy models of multimass electromechanical systems." Eastern-European Journal of Enterprise Technologies 3, no. 2(99) (2019): 51–63. https://doi.org/10.15587/1729-4061.2019.169080.

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The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study. A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesiz

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Vladareanu, Victor, Luige Vladareanu, Radu Ioan Munteanu, et al. "Adaptive neural network fuzzy inference system for HFC processes." Periodicals of Engineering and Natural Sciences (PEN) 7, no. 1 (2019): 311. http://dx.doi.org/10.21533/pen.v7i1.337.

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Wang, Xiu Yan, Ying Wang, and Zong Shuai Li. "Research of the 3-DOF Helicopter System Based on Adaptive Inverse Control." Applied Mechanics and Materials 389 (August 2013): 623–31. http://dx.doi.org/10.4028/www.scientific.net/amm.389.623.

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For the flight control problem occurred in 3-DOF Helicopter System, reference adaptive inverse control scheme based on Fuzzy Neural Network model is designed. Firstly, fuzzy inference process of identifier and controller is achieved by using the network structure. Meanwhile, the neural network connection weights are used to express parameters of fuzzy inference. Then, back-propagation algorithm is adopted to amend the network connection weights in order to automatically identify the fuzzy model and adjust its membership functions and parameters, so that the actual system output of adaptive inv

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Hessami, Masoud, François Anctil, and Alain A. Viau. "An adaptive neuro-fuzzy inference system for the post-calibration of weather radar rainfall estimation." Journal of Hydroinformatics 5, no. 1 (2003): 63–70. http://dx.doi.org/10.2166/hydro.2003.0005.

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An Adaptive Neuro-Fuzzy Inference System, based on a jack-knife approach, is proposed for the post-calibration of weather radar rainfall estimation exploiting available raingauge observations. The methodology relies on the construction of a fuzzy inference system with three inputs (radar x coordinate, y coordinate and rainfall estimation at raingauge locations) and one output (raingauge observations). Subtractive clustering is used to generate the initial fuzzy inference system. Artificial neural network learning provides a fast way to automatically generate additional fuzzy rules and membersh

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Yang, Xiao Bo. "Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems." Advanced Materials Research 332-334 (September 2011): 1505–10. http://dx.doi.org/10.4028/www.scientific.net/amr.332-334.1505.

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In this paper, a new method of subtractive clustering adaptive network fuzzy inference systems is proposed to assess degree of wrinkle in the fabric. The clustering center can be gotten through subtractive clustering algorithm, which is the base to set up adaptive network inference systems. Firstly, subtractive clustering algorithm is used to confirm the structure of fuzzy neural network, then, fuzzy inference system is used to process pattern recognition. Finally, four kinds of fabric wrinkle feature parameters are used to verify the results on real fabric. The results show the applicability

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Gorbachev, Sergey, and Vladimir Syryamkin. "Adaptive Neuro-Fuzzy Recognition Technology Intersecting Objects." Applied Mechanics and Materials 756 (April 2015): 683–88. http://dx.doi.org/10.4028/www.scientific.net/amm.756.683.

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The study discusses adaptive neuro-fuzzy methods of recognition of the multidimensional overlapping objects on the basis of the introduced concepts, generalized and modified operations of fuzzy set theory and neural networks. To improve recognition accuracy, proposed a combined approach including neural network analysis of generalized images based on Kohonen maps, and building systems fuzzy inference based on the identification of allocated clusters integral characteristics of the images. Using the derived system of diagnostic decision rules "If ... then" the comprehensive forecast map of oil

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Meriem, Fikri, Sabri Omar, and Cheddadi Bouchra. "Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5749–57. https://doi.org/10.11591/ijece.v10i6.pp5749-5757.

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In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; artificial n

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15

Liang, Zhongwei, Xiaochu Liu, Guilin Wen, and Jinrui Xiao. "Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 234, no. 13 (2020): 1615–39. http://dx.doi.org/10.1177/0954405420927582.

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Abrasive jetting stream generated from accelerator tank is crucial to the precision machining of industrial products during the process of strengthen jet grinding. In this article, its effectiveness prediction using normalized sparse autoencoder-adaptive neural fuzzy inference system is carried out to provide an optimal result of jetting stream. A normalized sparse autoencoder-adaptive neural fuzzy inference system capable of calculating the concentration density of abrasive impact stress by normalized sparse autoencoder and identifying the effectiveness indexes of abrasive jetting by adaptive

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Zhang, Su Ying, Ying Wang, Jie Liu, and Xiao Xue Zhao. "Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum." Advanced Materials Research 765-767 (September 2013): 2004–7. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2004.

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Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functio

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Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decompos

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Chen, Xiang Ping. "Study on Adaptive Fuzzy Control System of Settlement Technology." Advanced Materials Research 1061-1062 (December 2014): 904–7. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.904.

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Considering the production status of red mud at present, an adaptive fuzzy control system, according to fuzzy control and genetic algorithm, has been focused on. With the control of flocculants, the system fuzzy control clarity of clear solution. Adaptive neural network fuzzy inference theory is adopted to establish the mathematical model of controlled object "black box", and MATLAB for simulation, showing that the control method has good accuracy and dynamic control quality. Satisfy the requirements of practice work.

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Titov, Andrei P. "ANALYSIS OF MODELS OF ADAPTIVE NEURO-FUZZY SYSTEMS." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2024): 21–35. http://dx.doi.org/10.28995/2686-679x-2024-1-21-35.

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The article deals with the study of basic methods for models of adaptive neuro-fuzzy systems. Based on the analysis, the strengths of neural networks and fuzzy logic were found, that became powerful tools for solving complex modeling and forecasting issues. There is studying and analyzing the adaptive neural network, which is a class of neural networks that have the ability to change their structure and parameters in the process of learning and adaptation to new data and conditions and besides the article studies the Gaussian membership function, also known as the normal membership function or

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Araújo Júnior, José M., Leandro L. S. Linhares, Fábio M. U. Araújo, and Otacílio M. Almeida. "Fuzzy wavelet neural networks applied as inferential sensors of neonatal incubator dynamics." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 2567–79. http://dx.doi.org/10.3233/jifs-190129.

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Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wa

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Madhura, Madhura, Asha KS, Mary Christeena Thomas, Anubhav Bhalla, Rajat Saini, and Aws Zuhair Sameen. "An Effective Internet of Things based Assessment of ANN and ANFIS algorithms for Cardiac Arrhythmia." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 99–110. http://dx.doi.org/10.54216/jisiot.130108.

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Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods:

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Nireekshana, Namburi, R. Ramachandran, and G. V. Narayana. "Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation." E3S Web of Conferences 472 (2024): 02005. http://dx.doi.org/10.1051/e3sconf/202447202005.

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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested A

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Nayak, Narayan, Soumya Ranjan Das, Tapas Kumar Panigrahi, et al. "Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle." Mathematics 11, no. 8 (2023): 1868. http://dx.doi.org/10.3390/math11081868.

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In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and

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Jha, Panchand. "Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS." IAES International Journal of Robotics and Automation (IJRA) 4, no. 2 (2015): 109. http://dx.doi.org/10.11591/ijra.v4i2.pp109-123.

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<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kin

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Rina, Nopianti, Tri Panudju Andreas, and Permana Angrian. "Stock Price Index Prediction Using Adaptive Neural Fuzzy Inference System." International Journal of Management, Accounting and Economics 8, no. 10 (2022): 715–32. https://doi.org/10.5281/zenodo.5907662.

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This paper aims to predict stock prices using open, high, low, close variables using artificial neural networks, especially the adaptive fuzzy neural inference system (ANFIS). Each stock has a different pattern and can be predicted if you have complete data. This study is limited by stock data for 2012-2019. The survey was conducted to collect stock data from the Yahoo Finance website. The stock data used is data from 2001-2018. Learning patterns of data patterns using the Adaptive Neural Fuzzy Inference System (ANFIS) were compared with regression analysis, Mean Square Error (MSE) and Mean Pr

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Fikri, Meriem, Omar Sabri, and Bouchra Cheddadi. "Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5749. http://dx.doi.org/10.11591/ijece.v10i6.pp5749-5757.

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In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; Artificial N

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Sharma, Jitender, Sonia, Karan Kumar, Pankaj Jain, Raed H. C. Alfilh, and Hussein Alkattan. "Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems." Mesopotamian Journal of CyberSecurity 5, no. 1 (2025): 1–10. https://doi.org/10.58496/mjcs/2025/001.

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Network security has become increasingly critical in recent years. Among the various aspects of network security and considering several approaches to network security, intrusion detection systems (IDSs) have gained considerable attention. The prominence of this factor, among other factors of network security, is due to its ability to address the complex and uncertain nature of security breaches. Whenever data flow over the network, precise categorization of normal and malicious data is necessary. Past IDS systems lack precise categorization. Thus, the present study focuses on the use of the a

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Ganesh, Aman, Ratna Dahiya, and Girish Kumar Singh. "Wide area adaptive hybrid fuzzy STATCOM controller for dynamic stability enhancement." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 5 (2016): 1830–49. http://dx.doi.org/10.1108/compel-07-2015-0249.

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Purpose The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system. Design/methodology/approach In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide ar

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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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Chirkena, Kefyalew Bali, Fatma Kaya Yıldırım, and Beyza Hatice Ulusoy. "Artificial intelligence-based model for evaluating the inhibition of Listeria monocytogenes, <i>Staphylococcus aureus</i>, and <i>Escherichi</i>a coli in kefir matrix." Quality Assurance and Safety of Crops & Foods 16, no. 4 (2024): 80–98. http://dx.doi.org/10.15586/qas.v16i4.1459.

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The present study aimed to inhibit the activity of some foodborne pathogens by probiotic lactic acid bacteria (LAB) in kefir. The antimicrobial effect of probiotic LAB was evaluated by using Artificial Intelligence (AI)-based models, Artificial Neural Network (ANN), and Adaptive Network-based Fuzzy Inference System (ANFIS). The experiment was performed on fermentation day 0, 1, and 2, and storage day 1, 3, 7, and 10 of kefir. The average inhibition results obtained for Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli at the training stage were 2.4 log10 CFU/g, 2.0 log10 CFU/

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B, Sivaranjani, and Kalaiselvi C. "SOBEL OPERATOR AND PCA FOR NEAREST TARGET OF RETINA IMAGES." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2483–91. http://dx.doi.org/10.21917/ijivp.2021.0353.

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In eye, innermost layer is retina. Various important anatomical structures are available in this. Different eye diseases like diabetic retinopathy, glaucoma, etc are indicated by this. For clinical study, patient screening, and diagnosing ocular diseases, physicians are assisted by vascular intersections and blood vessels extraction in retinal images. Retina image’s nearest template are detected using fuzzy neural network (FNN), Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensemble in recent work. However, various factors like low contrast a

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Sahoo, Satyabrata, Bidyadhar Subudhi, and Gayadhar Panda. "Torque and pitch angle control of a wind turbine using multiple adaptive neuro-fuzzy control." Wind Engineering 44, no. 2 (2019): 125–41. http://dx.doi.org/10.1177/0309524x19849825.

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This article presents a multiple adaptive neuro-fuzzy inference system-based control scheme for operation of the wind energy conversion system above the rated wind speed. By controlling the pitch angle and generator torque concurrently, the generator power and speed fluctuation can be reduced and also turbine blade stress can be minimized. The proposed neuro-fuzzy-based adaptive controller is composed of both the Takagi–Sugeno fuzzy inference system and neural network. First, a step change in wind speed and then a simulated wind speed are considered in the proposed adaptive control design. A M

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ALEXANDRIDIS, ALEX. "EVOLVING RBF NEURAL NETWORKS FOR ADAPTIVE SOFT-SENSOR DESIGN." International Journal of Neural Systems 23, no. 06 (2013): 1350029. http://dx.doi.org/10.1142/s0129065713500299.

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This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input–output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forge

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Tiab Ali. "Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network." Panamerican Mathematical Journal 35, no. 1s (2024): 103–14. http://dx.doi.org/10.52783/pmj.v35.i1s.2119.

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The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a mul

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Abdullah, Abu Hassan, Sukhairi Sudin, Saad Fathinul Syahir Ahmad, Hassan Muhamad Khairul Ali, MUHAMMAD IMRAN AHMAD, and Khalid Kamarul Aizat Abdul. "Aquaculture monitoring system using multi-layer perceptron neural network and adaptive neuro fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 71–81. https://doi.org/10.11591/ijeecs.v33.i1.pp71-81.

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The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based database. Th

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Chidambaram, S., S. Sankar Ganesh, Alagar Karthick, Prabhu Jayagopal, Bhuvaneswari Balachander, and S. Manoharan. "Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System." Computational and Mathematical Methods in Medicine 2022 (May 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/9166873.

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In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is appl

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Pham, Duc-Anh, and Seung-Hun Han. "Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System." Journal of Marine Science and Engineering 11, no. 7 (2023): 1262. http://dx.doi.org/10.3390/jmse11071262.

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Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study pre

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

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

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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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Hassan Abdullah, Abu, Sukhairi Bin Sudin, Fathinul Syahir Ahmad Saad, Muhamad Khairul Ali Hassan, Muhammad Imran Ahmad, and Kamarul Aizat bin Abdul Khalid. "Aquaculture monitoring system using multi-layer perceptron neural network and adaptive neuro fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 71. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp71-81.

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<span>The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based

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Mlakić, Dragan, Srete N. Nikolovski, and Goran Knežević. "An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1294. http://dx.doi.org/10.11591/ijece.v6i3.10147.

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The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)

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Mlakić, Dragan, Srete N. Nikolovski, and Goran Knežević. "An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1294. http://dx.doi.org/10.11591/ijece.v6i3.pp1294-1304.

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The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)

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Ansaf, Huda, Bahaa Kazem Ansaf та Sanaa S. Al Samahi. "A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus". BioChem 1, № 3 (2021): 159–73. http://dx.doi.org/10.3390/biochem1030013.

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This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership funct

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Katiyar, Kartik, Ayush Ayush, Deep Singh, and Supriya Sharma. "POWER MANAGEMENT STRATEGY BASED ON ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR AC MICROGRID." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (2021): 288–91. http://dx.doi.org/10.53633/ijmasri.2021.1.10.006.

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Micro grids increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources integrated into the MGs usually results in different problems. We are trying to achieve a stable system which can power microgrids and can be of commercial use. This would be achieved by simulation of the system under different climatic conditions. Keywords: Microgrid Optimization, ANFIS, Neural Network, Fuzzy Logic

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Lachouri, Charaf Eddine, Khaled Mansouri, and Mohamed M. Lafifi. "Greenhouse Climate Modeling Using Fuzzy Neural Network Machine Learning Technique." Revue d'Intelligence Artificielle 36, no. 6 (2022): 925–30. http://dx.doi.org/10.18280/ria.360614.

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The greenhouse climate is a non-linear system that contains multiple inputs (predictors) and multiple outputs (responses). This project aimed to provide a solution, aided by artificial intelligence, to the issue of variations in time, input and output factors in a greenhouse internal climate that can adversely affect tomato seedlings. Machine learning Methodologies such as fuzzy inference and neural networks have been applied to mimic idealistic behavior. This paper proposes an adaptive system based on artificial neural networks technique embedded with fuzzy logic technique calls Adaptive Neur

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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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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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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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Sui, Wen Tao, and Dan Zhang. "Fault Diagnosis of Roller Bearing Conditions Using ANFIS." Applied Mechanics and Materials 16-19 (October 2009): 886–90. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.886.

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This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

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CHEMACHEMA, MOHAMED, and KHALED BELARBI. "ROBUST DIRECT ADAPTIVE CONTROLLER FOR A CLASS OF NONLINEAR SYSTEMS BASED ON NEURAL NETWORKS AND FUZZY LOGIC SYSTEMS." International Journal on Artificial Intelligence Tools 16, no. 03 (2007): 553–60. http://dx.doi.org/10.1142/s0218213007003412.

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In this paper a direct adaptive control algorithm based on a neural network NN as controller and a fuzzy inference system FIS as control error estimator is presented for a class of SISO uncertain nonlinear systems. The weights adaptation laws are based on the control error. A fuzzy inference system is used to provide an estimate of this error based on past history of the system behavior. The stability of the closed loop is studied using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach.

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