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TASK Quarterly, 2003 In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central limit theorem, is also shown.
ADEL M. ALIMI
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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
L, Rutkowski, K, Cpalka
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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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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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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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Study the Robustness of Automatic Voltage Regulator for Synchronous Generator Based on Neuro-Fuzzy Network [PDF]
Engineering and Technology Journal, 2015 Modern power systems are complex and non-linear and their operating conditions can vary over a wide range, and since neuro - fuzzy networkcan be used as intelligent controllers to control non-linear dynamic systems through learning, which can easily ...
Abdulrahim Thiab Humod +1 more
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Performance of heterogenous neuro-fuzzy ensembles over medical datasets
Scientific African, 2023 Neuro-fuzzy systems combine the abilities of both artificial neural networks and fuzzy systems. They are easily trainable and provide a certain level of interpretability.
Hicham Benbriqa +2 more
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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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WRNFS: Width Residual Neuro Fuzzy System, a Fast-Learning Algorithm with High Interpretability
Applied Sciences, 2022 Although the deep neural network has a strong fitting ability, it is difficult to be applied to safety-critical fields because of its poor interpretability. Based on the adaptive neuro-fuzzy inference system (ANFIS) and the concept of residual network, a
Lingkun Kong, Dewang Chen, Ruijun Cheng
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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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