Relevant bibliographies by topics / Adaptive Neural Network Fuzzy Inference System

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Academic literature on the topic 'Adaptive Neural Network Fuzzy Inference System'

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Published: 3 June 2025
Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "Adaptive Neural Network Fuzzy Inference System"

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Weeraprajak, Issarest. "Faster Adaptive Network Based Fuzzy Inference System." Thesis, University of Canterbury. Mathematics and Statistics, 2007. http://hdl.handle.net/10092/1234.

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It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the pa
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases
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Jahankhani, Pari. "Development of a decision support framework for electroencephalography signals based on an adaptive fuzzy inference neural network system." Thesis, University of Westminster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507837.

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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.

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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural
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Ollé, Tamás. "Klasifikace vzorů pomocí fuzzy neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219728.

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Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.
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Mohammadzadeh, Soroush. "System identification and control of smart structures: PANFIS modeling method and dissipativity analysis of LQR controllers." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/868.

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"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy in
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Lau, Chun Yin. "Extended adapative [i.e. adaptive] neuro-fuzzy inference systems." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20070130.170625/index.html.

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Xu, Andong. "Flexible adaptive-network-based fuzzy inference system." Diss., Online access via UMI:, 2006.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2006.<br>Includes bibliographical references.
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Vasilic, Slavko. "Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/436.

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This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combin
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Štechová, Edita. "Application of the Artificial Intelligence in the Real Estate Valuation." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-192596.

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The main purpose of this study is to develop a predictive model capable to forecast residential real estate prices in the city of Prague using Artificial Intelligence methods. The first part of this study discusses fundamentals of Artificial Neural Networks and Fuzzy Inference Systems in the context of real estate valuation. The second part demonstrates a development and testing of such models using a dataset of real estate market transactions. In the third part, results are compared to Multiple Regression and an explanatory power of each model is evaluated. Conclusions of this research are: (
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Book chapters on the topic "Adaptive Neural Network Fuzzy Inference System"

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Brahim, Kais, and Andreas Zell. "ANFIS-SNNS: Adaptive Network Fuzzy Inference System in the Stuttgart Neural Network Simulator." In Fuzzy-Systems in Computer Science. Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-86825-1_9.

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Jahankani, Pari, Vassilis Kodogiannis, and John Lygouras. "Adaptive Fuzzy Inference Neural Network System for EEG Signal Classification." In Handbook on Decision Making. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13639-9_18.

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Minghui, Wang, Yu Yongquan, and Lin Wei. "Adaptive Neural-Based Fuzzy Inference System Approach Applied to Steering Control." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_136.

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D’Amato, Valeria, Gabriella Piscopo, and Maria Russolillo. "Adaptive Neuro-Fuzzy Inference Systems vs. Stochastic Models for Mortality Data." In Recent Advances of Neural Network Models and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04129-2_25.

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Kurnaz, Sefer, Okyay Kaynak, and Ekrem Konakoğlu. "Adaptive Neuro-Fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles." In Advances in Neural Networks – ISNN 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_3.

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Lara, Juan A., Pari Jahankhani, Aurora Pérez, Juan P. Valente, and Vassilis Kodogiannis. "Classification of Stabilometric Time-Series Using an Adaptive Fuzzy Inference Neural Network System." In Artificial Intelligence and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13208-7_79.

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Pasha, Muhammad Fermi, Rahmat Budiarto, Mohammad Syukur, and Masashi Yamada. "EFIS: Evolvable-Neural-Based Fuzzy Inference System and Its Application for Adaptive Network Anomaly Detection." In Advances in Machine Learning and Cybernetics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_69.

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Roy, Etee Kawna, and Subrata Kumar Aditya. "Prediction of Acute Myeloid Leukemia Subtypes Based on Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Approaches." In Lecture Notes in Networks and Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8204-7_43.

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Babu, Prathibha S., Sangeetha Subhash, and K. Ilango. "SOC Estimation of Li-Ion Battery Using Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0915-5_27.

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Mishra, Pratishtha, and Pijush Samui. "Reliability Analysis of Retaining Wall Using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Lecture Notes in Civil Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6370-0_48.

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Conference papers on the topic "Adaptive Neural Network Fuzzy Inference System"

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G., Nivedhitha, Pranav Karthikeyan, and K. R. M. Vijaya Chandrakala. "Adaptive Cruise Control System in Transportation Systems using Artificial Neural Network based Fuzzy Inference System." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984772.

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Zhang, Xuewei, Long Long, Xuancheng Liu, Di Xu, Qiliang Yang, and Jidong Ge. "Damage Prediction of Underground Engineering by Adaptive Network Based Fuzzy Inference System." In 2024 IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2024. https://doi.org/10.1109/iccsse63803.2024.10823774.

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Radhi, Ahmed A. "Improve the Quad copter Stability by Using An Adaptive Fuzzy Inference Neural Network (AFINN)." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10748033.

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QIN, YI, and ZHENG PEI. "A NEW ADAPTIVE FUZZY INFERENCE NEURAL NETWORK." In Proceedings of the 4th International ISKE Conference on Intelligent Systems and Knowledge Engineering. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295062_0103.

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Krizea, Maria, John Gialelis, and Stavros Koubias. "Comparative study between Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System and Neural Network for Healthcare Monitoring." In 2019 8th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2019. http://dx.doi.org/10.1109/meco.2019.8760050.

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Honda, Katsuhiro, Koki Kitamori, Seiki Ubukata, and Akira Notsu. "A Noise Clustering-induced Robust Adaptive Network-based Fuzzy Inference System for Classification." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892766.

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Tyzhuk, Denys, and Maryna Filippova. "ANALYSIS OF THE PARAMETERS OF THE AUTOMATED CONTROL SYSTEM FOR MANAGING THE CABLE PRODUCTION PROCESS." In 17th IC Measurement and Control in Complex Systems. VNTU, 2024. https://doi.org/10.31649/mccs2024.5-02.

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An important element of most technical systems are communication channels containing communication lines, in which conductive cables are usually used: radio frequency, coaxial and data transmission cables. Their production is a continuous multi-operational process that has all the properties of a complex system. Most cable companies use programmable logic controllers with conventional controllers to control line speed during cable extrusion. These traditional control rollers have difficulty maintaining a constant line speed, causing surface defects on the extruded cables and affecting the qual
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"Network Traffic Prediction based on Adaptive Neural Fuzzy Inference Systems." In 2015 The 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering. WCSE, 2015. http://dx.doi.org/10.18178/wcse.2015.04.109.

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Cardenas-Maciel, Selene L., Oscar Castillo, Luis T. Aguilar, and Juan R. Castro. "A T-S Fuzzy Logic Controller for biped robot walking based on adaptive network fuzzy inference system." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596653.

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Atique, Mohammad, and Mir Sadique Ali. "A Novel Adaptive Neuro Fuzzy Inference System Based CPU Scheduler for Multimedia Operating System." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371095.

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