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Journal articles on the topic 'Neuro-fuzzy logic'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks

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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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3

Katayama, Ryu. "Applications of Neuro Fuzzy Technology in Consumer Electronics Products." Journal of Robotics and Mechatronics 7, no. 1 (1995): 2–8. http://dx.doi.org/10.20965/jrm.1995.p0002.

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In recent years, intelligent industrial systems and consumer electronic products have been widely and intensively developed. Fuzzy logic, neural network, and neuro fuzzy technology, which integrates both approaches, are now regarded as an effective method to realize such intelligent features. In this paper, a review of the fuzzy boom in the consumer electronics market of Japan is presented. Typical applications of home appliances using fuzzy logic and neuro fuzzy technology are then described. Finally, methods and tools for developing fuzzy systems such as self-tuning and fuzzy modeling are re

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Chandrasekhar, Tadi, and Ch Sumanth Kumar. "Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System." Indian Journal Of Science And Technology 16, no. 13 (2023): 1014–20. http://dx.doi.org/10.17485/ijst/v16i13.1833.

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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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Jindal, Nikita, Jimmy Singla, Balwinder Kaur, et al. "Fuzzy Logic Systems for Diagnosis of Renal Cancer." Applied Sciences 10, no. 10 (2020): 3464. http://dx.doi.org/10.3390/app10103464.

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Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the curren

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Ashigwuike, Evans Chinemezu, and Stephen Adole Benson. "Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique." European Journal of Engineering Research and Science 4, no. 4 (2019): 83–89. http://dx.doi.org/10.24018/ejers.2019.4.4.1237.

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The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile

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Ashigwuike, Evans Chinemezu, and Stephen Adole Benson. "Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique." European Journal of Engineering and Technology Research 4, no. 4 (2019): 83–89. http://dx.doi.org/10.24018/ejeng.2019.4.4.1237.

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The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile

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9

Biswas, Saroj, Monali Bordoloi, and Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches." International Journal of Applied Evolutionary Computation 7, no. 4 (2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.

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This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective

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Болгов, А. А. "RISK ASSESSMENT USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 4(-) (December 23, 2022): 521–30. http://dx.doi.org/10.36622/vstu.2022.25.4.006.

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В работе предлагается использование адаптивной нейро-нечеткой системы вывода для оценки риска. Проводится подробный обзор адаптивной нейро-нечеткой системы вывода, выделяя основные свойства этой системы в области методов оценки рисков. Приведены основные преимущества использования адаптивной нейро-нечеткой системы вывода. Рассматривается архитектура адаптивной нейро-нечеткой системы вывода. Выделены и рассмотрены основные методы обучения системы. Предложены методы оценки эффективности модели на основе адаптивной нейро-нечеткой системы вывода для оценки риска. Представлен алгоритм внедрения ада

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Ozguven, Omer Faruk, Mehmet Salih Mamis, and Arif Memmedov. "An experimental fuzzy logic application of position control using ST52 microcontroller." UNEC Journal of Engineering and Applied Sciences 4, no. 2 (2024): 76–90. https://doi.org/10.61640/ujeas.2024.1208.

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Position control is required in many industrial applications. In this paper, several control methods; namely servo, fuzzy logic and neuro-fuzzy position controls implemented in ST52 microcontroller and laboratory test results of these methods are introduced. Fuzzystudio3.0 and Adaptive Fuzzy Modeller software packages have been used for the program development. The response of designed control systems to any desired position changes is measured, and the performance and speed for fuzzy, neuro-fuzzy and servo controllers in setting a desired position are examined. The designed software and insta

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Chang, Wen-Jer. "Special Issue “Application of Fuzzy Control in Computational Intelligence”." Processes 10, no. 12 (2022): 2522. http://dx.doi.org/10.3390/pr10122522.

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Due to the fitted structure of fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems, we can study computational intelligence [...]

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Simiński, Krzysztof. "Rough subspace neuro-fuzzy system." Fuzzy Sets and Systems 269 (June 2015): 30–46. http://dx.doi.org/10.1016/j.fss.2014.07.003.

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Smoczek, Jarosław, and Janusz Szpytko. "The Application of a Neuro-Fuzzy Adaptive Crane Control System." Journal of Konbin 14-15, no. 1 (2010): 247–58. http://dx.doi.org/10.2478/v10040-008-0182-8.

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The Application of a Neuro-Fuzzy Adaptive Crane Control SystemThe unconventional methods, mostly based on fuzzy logic, are often addressed to a problem of anti-sway crane control. The problem of practical application of those solutions is important owing to come the growing expectations for time and precision of transportation operations and exploitation quality of material handling devices. The paper presents the designing methods of an adaptive anti-sway crane control system based on the neuro-fuzzy controller, as well as the software and hardware equipments used to aid the programming reali

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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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Nguyen, Khoa Dang, and Duong Quang Do. "EXTRACTING CAUSE-EFFECT RELATIONSHIPS IN PHARMACY PRODUCTS USING NEURO-FUZZY SYSTEM COMBINED TO VISUALIZATION TECHNIQUE." Science and Technology Development Journal 13, no. 1 (2010): 35–42. http://dx.doi.org/10.32508/stdj.v13i1.2093.

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Neuro-fuzzy system is a fusion functionalities in neural networks and fuzzy logic in order to model and extract knowledge from data. This research presents an application of neuro-fuzzy combined to visualization approach for extracting cause-effect relationships between ingredients and properties in formulation. This result will lead formulators to understanding their products more precisely and saving a lot of time and labor in R&D process.

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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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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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Astuti, Winda Try, Much Aziz Muslim, and Endang Sugiharti. "The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas." Scientific Journal of Informatics 6, no. 1 (2019): 95–105. http://dx.doi.org/10.15294/sji.v6i1.16648.

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The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of

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Petković, Biljana, Dalibor Petković, Boris Kuzman, et al. "Neuro-fuzzy estimation of reference crop evapotranspiration by neuro fuzzy logic based on weather conditions." Computers and Electronics in Agriculture 173 (June 2020): 105358. http://dx.doi.org/10.1016/j.compag.2020.105358.

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Nilashi, Mehrbakhsh, Fausto Cavallaro, Abbas Mardani, Edmundas Zavadskas, Sarminah Samad, and Othman Ibrahim. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique." Sustainability 10, no. 8 (2018): 2707. http://dx.doi.org/10.3390/su10082707.

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Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this techn

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22

Chen, Minyou, and D. A. Linkens. "A hybrid neuro-fuzzy PID controller." Fuzzy Sets and Systems 99, no. 1 (1998): 27–36. http://dx.doi.org/10.1016/s0165-0114(96)00401-0.

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Arafeh, L., H. Singh, and S. K. Putatunda. "A neuro fuzzy logic approach to material processing." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 29, no. 3 (1999): 362–70. http://dx.doi.org/10.1109/5326.777072.

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Tserkovny, Alex. "A Neuro T-Norm Fuzzy Logic Based System." Journal of Software Engineering and Applications 17, no. 08 (2024): 638–63. http://dx.doi.org/10.4236/jsea.2024.178035.

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Rowe, Raymond C., and Christopher G. Woolgar. "Neuro-fuzzy logic in tablet film coating formulation." Pharmaceutical Science & Technology Today 2, no. 12 (1999): 495–97. http://dx.doi.org/10.1016/s1461-5347(99)00224-2.

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Theisen, M., A. Steudel, M. Rychetsky, and M. Glesner. "Fuzzy Logic and Neuro-Systems Assisted Intelligent Sensors." Sensors Update 3, no. 1 (1998): 29–59. http://dx.doi.org/10.1002/1616-8984(199801)3:1<29::aid-seup29>3.0.co;2-4.

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R. Wade, Cooper, Daniel Kelly-B Danquah, Hossein Salehfar, and Olusegun S. Tomomewo. "Neuro-Fuzzy Logic Applications for Grid Energy Management." American Journal of Systems and Software 6, no. 1 (2023): 1–10. http://dx.doi.org/10.12691/ajss-6-1-1.

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

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

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Nebot, Àngela, and Francisco Mugica. "Forest Fire Forecasting Using Fuzzy Logic Models." Forests 12, no. 8 (2021): 1005. http://dx.doi.org/10.3390/f12081005.

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In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature.

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

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

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Fan, Ya-Jun, Hai-tong Xu, and Zhao-Yu He. "Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller." Energy Exploration & Exploitation 40, no. 2 (2021): 539–53. http://dx.doi.org/10.1177/01445987211041779.

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Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the

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Subudhi, B., and A. S. Morris. "Fuzzy and neuro-fuzzy approaches to control a flexible single-link manipulator." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 217, no. 5 (2003): 387–99. http://dx.doi.org/10.1177/095965180321700505.

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In this paper, new fuzzy and neuro-fuzzy approaches to tip position regulation of a flexible-link manipulator are presented. Firstly, a non-collocated, proportional-dervative (PD) type, fuzzy logic controller (FLC) is developed. This is shown to perform better than typical model-based controllers (LQR and PD). Following this, an adaptive neuro-fuzzy controller (NFC) is described that has been developed for situations where there is payload variability. The proposed NFC tunes the input and output scale parameters of the fuzzy controller on-line. The efficacy of the NFC has been evaluated by com

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Kumar, Neeraj. "Comparative Analysis of Different Controllers for Tracking of Manipulator." Journal of Futuristic Sciences and Applications 5, no. 1 (2022): 36–41. http://dx.doi.org/10.51976/jfsa.512205.

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Engineers have struggled to control robots since the 1950s, when the PID controller was first used to regulate complex systems. Since its release, this controller has been a top pick among manufacturers due to its low price and ease of assembly. For nonlinear systems, fuzzy logic controllers have been used by researchers and scientists to overcome the drawbacks of PID. However, as time goes on, new controlling techniques develop that are more powerful in terms of control than the previous ones. Complex, nonlinear, and dynamically ununderstood systems need neuro logic controllers, which, when p

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Et. al., T. Arul Raj,. "A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 1037–46. http://dx.doi.org/10.17762/turcomat.v12i6.2416.

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Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The propos

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Braz-César, Manuel, and Rui Barros. "Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure." International Journal of Structural Stability and Dynamics 17, no. 05 (2016): 1740007. http://dx.doi.org/10.1142/s0219455417400077.

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Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been wid

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Saatchi, Reza. "Fuzzy Logic Concepts, Developments and Implementation." Information 15, no. 10 (2024): 656. http://dx.doi.org/10.3390/info15100656.

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Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuz

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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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Imene, Djelamda, and Bouchareb Ilhem. "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): 1892~1901. https://doi.org/10.11591/eei.v11i4.3818.

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Permanent magnet synchronous motor (PMSM) speed control is generally done using field oriented control, which uses conventional proportionalintegral (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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Carvalho, Lucimar M. F. de, Silvia Modesto Nassar, Fernando Mendes de Azevedo, Hugo José Teixeira de Carvalho, Lucas Lese Monteiro, and Ciciliana M. Zílio Rech. "A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations." Arquivos de Neuro-Psiquiatria 66, no. 2a (2008): 179–83. http://dx.doi.org/10.1590/s0004-282x2008000200007.

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OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%)

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Ibemezie, Ndubuisi Paul-Darlington, Julius Egwu Arua, Igwe Lazarus Uduma, John Ukanu, Ali, Uche Egwu, and Ukoima Katoubokmelek Thompson. "Comparative Evaluation of Intelligent Agent Based Improved Control Designs of Electro-pneumatic Clutch Actuation System for Heavy Duty Vehicles." Journal of Engineering Research and Reports 27, no. 5 (2025): 138–53. https://doi.org/10.9734/jerr/2025/v27i51499.

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The comparative performances of electro-pneumatic clutch actuation system in heavy-duty vehicles using intelligent agent-based control adaptation technique is presented. Conventional control techniques in clutch actuation uses on/off, servo mechanism and other non-intelligent methods of actuation control. These techniques demand for calibration of clutch actuators. To eliminate calibration and its defects, intelligent control methods of clutch actuation are implemented. The specific methodology was predicated on three intelligent agent systems of Fuzzy logic, Neural Network and Hybrid Neuro-Fu

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KOPRINKOVA-HRISTOVA, PETIA. "BACKPROPAGATION THROUGH TIME TRAINING OF A NEURO-FUZZY CONTROLLER." International Journal of Neural Systems 20, no. 05 (2010): 421–28. http://dx.doi.org/10.1142/s0129065710002504.

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The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters ration

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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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Li, Jun Wei, Hai Yan Shen, and Huian Sun. "The Design of Neuro-Fuzzy Controller for Active Suspension System." Applied Mechanics and Materials 330 (June 2013): 673–76. http://dx.doi.org/10.4028/www.scientific.net/amm.330.673.

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A neuro-fuzzy control (NFC) system is developed to control the suspension system of vehicle due to its nonlinearity and parameter variations. A neural network (NN) is used to adjust the premise parameters and the consequent parameters in fuzzy logic control (FLC). Simulation results by using NFC are compared with those of the conventional PID controller and passive suspension system. Based on the simulation, it can be concluded that the neuro-fuzzy controller shows a good performance in both passenger comfort and vehicle handing in comparison to the conventional PID controller and passive susp

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Pandey, Arun Kumar, and Avanish Kumar Dubey. "Neuro Fuzzy Modeling of Laser Beam Cutting Process." Applied Mechanics and Materials 110-116 (October 2011): 4109–17. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.4109.

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Laser Beam Cutting (LBC) being a complex cutting process needs a reliable model for prediction of the process performance. This research work presents a modeling study of LBC process. A hybrid approach of Artificial Neural Network (ANN) and Fuzzy Logic (FL) has been used for developing the Kerf width model. The developed Neuro Fuzzy model of Kerf width has also been compared with Response Surface Methodology (RSM) based model and it has been found that the values of Kerf width predicted by the Neuro Fuzzy Model are more closer to the experimental values.

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Zhang, Dong, Xiao-Li Bai, and Kai-Yuan Cai. "Extended neuro-fuzzy models of multilayer perceptrons." Fuzzy Sets and Systems 142, no. 2 (2004): 221–42. http://dx.doi.org/10.1016/s0165-0114(03)00244-6.

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Paiva, Rui Pedro, and António Dourado. "Interpretability and learning in neuro-fuzzy systems." Fuzzy Sets and Systems 147, no. 1 (2004): 17–38. http://dx.doi.org/10.1016/j.fss.2003.11.012.

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Banakar, Ahmad, and Mohammad Fazle Azeem. "Parameter identification of TSK neuro-fuzzy models." Fuzzy Sets and Systems 179, no. 1 (2011): 62–82. http://dx.doi.org/10.1016/j.fss.2011.05.003.

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Ogutu, Patrick O. M., Nicholas Oyie, and Dr Winston Ochieng Ojenge. "CHICKEN BANDA PERFORMANCE IMPROVEMENT UTILIZING NEURO-FUZZY LOGIC TECHNIQUE." Journal of Research in Engineering and Applied Sciences 7, no. 3 (2023): 362–67. http://dx.doi.org/10.46565/jreas.202273362-367.

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This study is on improvement of performance of the chicken Banda, using indoor change in environmental conditions for temperature control. The differential change in climatic conditions is technically used to put on the fan and the Banda so as to realize the right comfortable indoor conditions. The chicken chicks’ Banda Mathematical model is created, prototype designed, temperature controller to depict a two systems simulation of neuro fuzzy logic and fuzzy logic .The performance is analyzed by the use of Matlab Simulink latest edition. To monitor the temperature of the Chicken cage the

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Shi, Yan, and Masaharu Mizumoto. "An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules." Fuzzy Sets and Systems 118, no. 2 (2001): 339–50. http://dx.doi.org/10.1016/s0165-0114(98)00440-0.

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Ломакина, Л. С., and И. Д. Чернобаев. "Neuro-fuzzy classifiers." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 9, no. 4(35) (2021): 27–28. http://dx.doi.org/10.26102/2310-6018/2021.35.4.027.

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В статье рассматривается проблема повышения точности искусственных нейронных сетей при решении задач классификации состояний объектов различной физической природы. Эту проблему предлагается сформулировать как проблему выбора типа функции активации в искусственных нейронных сетях и рассматривать ее с позиции теории нечетких множеств. В этой связи разработана математическая модель адаптивной функции активации искусственного нейрона, использующая нечеткую логическую систему с интервальными нечеткими множествами второго типа. Эта функция отличается от обыкновенных функций активации, применяемых в

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