Results 11 to 20 of about 63,417 (204)
ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers [PDF]
Open Engineering, 2016 In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS.
César Manuel Braz, Barros Rui Carneiro
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IMAGE DATA CLASSIFICATION BY NFES-MODEL [PDF]
Jurnal Informatika, 2008 In this paper, we propose an identification method of the land cover from remote sensing data with combining neuro-fuzzy and expert system. This combining then is called by Neuro-Fuzzy Expert System Model (NFES-Model).
M. Givi Efgivia, Safaruddin A. Prasad
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Flexible neuro-fuzzy systems [PDF]
IEEE Transactions on Neural Networks, 2003 In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and
Leszek Rutkowski, Krzysztof Cpalka
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Quality of service (QoS) for LTE network based on adaptive neuro fuzzy inference system
IET Communications, 2021 The main objective of this paper is to design an Adaptive Neuro Fuzzy Inference System model to calculate the quality of service for LTE HetNet applications. The quality of service parameters considered are delay, loss rate, throughput, and jitter.
Hala B. Nafea +2 more
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Scientific African, 2023 Purpose: Neuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using ...
Hafsaa Ouifak, Ali Idri
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Using an adaptive fuzzy-logic system to optimize the performances and the reduction of chattering phenomenon in the control of induction motor [PDF]
Serbian Journal of Electrical Engineering, 2009 Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance.
Barazane Linda +3 more
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Response surface methodology and adaptive neuro-fuzzy inference system for adsorption of reactive orange 16 by hydrochar [PDF]
Global Journal of Environmental Science and Management, 2023 BACKGROUND AND OBJECTIVES: The prediction models, response surface methodology and adaptive neuro-fuzzy inference system are utilized in this study. This study delves into the removal efficiency of reactive orange 16 using hydrochar derived from the ...
J. Oliver Paul Nayagam, K. Prasanna
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An indirect adaptive neuro-fuzzy speed control of induction motors [PDF]
Journal of Artificial Intelligence and Data Mining, 2016 This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and ...
M. Vahedi +2 more
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GrNFS: A Granular Neuro–Fuzzy System for Regression in Large Volume Data
International Journal of Applied Mathematics and Computer Science, 2021 Neuro-fuzzy systems have proved their ability to elaborate intelligible nonlinear models for presented data. However, their bottleneck is the volume of data. They have to read all data in order to produce a model.
Siminski Krzysztof
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Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant
Applied Sciences, 2022 One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training.
Helbert Espitia +2 more
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