Neuro-fuzzy modeling of deformation parameters for fusion-barriers

Akkoyun, Serkan; Torun, Yunis

doi:10.1016/j.net.2020.10.017

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…It is obvious that the membership functions required for the formation of fuzzy sets must be determined. ANFIS allows you to use data sets to determine the rule base and membership functions for this purpose [22][23][24][25]. The ANFIS system employs two approaches: NNs and FL, if these two systems are merged, they may obtain a successful outcome that involves either fuzzy intelligence or neural network analytical abilities.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Artificial Intelligence Approaches for Studying the pp Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Habashy¹,

El-Bakry²,

El‐Dahshan³

et al. 2023

Neuromorphic Computing

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Adaptive Neuro-Fuzzy Inference System (ANFIS), a popular machine learning model, is introduced in this chapter. ANFIS has a long development history and good agreement on scientific accomplishments. The value of ANFIS has grown dramatically along with the great interest in deep learning. We will examine how machine learning and ANFIS are related. Different methods can be used to implement machine learning models. ANFIS is a Fuzzy Inference System (FIS) that works within the context of adaptive networks. It merges the ideas of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. This framework can learn to estimate nonlinear functions and operates as a universal estimator. This chapter aimed to investigate the behavior of D mesons ratios production cross section (D+/D0,D∗+/D0,Ds+/D0,andDs+/D+), differential production cross section of prompt (D0,D+, D∗+andDs+ mesons) as a function of PT in pp collisions at (s = 5.02 and 7 TeV) and predict the behavior for others. The ANFIS model was created through a series of trial-and-error experiments. The ANFIS-based model simulation results perfectly fit the experimental data. When tested with non-training data points, the ANFIS prediction capabilities performed well. The ANFIS offers extensive procedures for high-energy physics modeling.

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Artificial Intelligence Approaches for Studying the pp Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Habashy¹,

El-Bakry²,

El‐Dahshan³

et al. 2023

Neuromorphic Computing

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“…Applications of this inference system in nuclear structure physics are almost non-existent. As an application example, Akkoyun et al [22] reviewed models of neuro-fuzzy deformation parameters for fusion barriers. Our study addresses a research gap within the field of nuclear structure physics, a sub-branch of nuclear technologies, by introducing the application of neuro-fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Inference and visualization of nuclear magnetic moment studies with neuro-fuzzy systems

Öztürk,

Kemah,

Yakut

et al. 2023

Phys. Scr.

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This study aims to predict the magnetic moments of nuclei with odd-A numbers in a certain region of which magnetic moment has not yet been calculated, using the Adaptive Neuro-Fuzzy Inference System (Anfis) method. In our Anfis model, the proton number (Z), neutron number (N), and spin value (I) are used as inputs for nuclei with 1≤Z≤88. With these input data, 526 odd-A nuclei are trained, and 124 nuclei are tested. The fact that the Anfis model was closer to the experimental data in the training and testing processes than the theoretical methods encouraged us to make inferences about the nuclei of which experimental nuclear magnetic moment is unknown. To that end, inferences have been made for 165 nuclei without experimental magnetic moments in regions 1≤Z≤28. In this region, Na, F, and P isotopes have been specifically studied. Magnetic moment value inferences made for these isotopes using Anfis have also been compared with the theoretical results of the Quasiparticle-Phonon Nuclear Method (QPNM) and with the Shell Model calculations. There is a satisfactory agreement between our predictions and the results of these two theories. Besides, the values obtained from the test and train operations are within the minimum error limits (~%0.03-0.04), the reliability of our system has been proven and a study that has not yet been done in the literature has been conducted. Since the Neuro-Fuzzy system will be a first in the field of nuclear technologies, we believe that the outputs of our study will be a good reference for future studies.

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From experimentation to prediction: comprehensive study of dielectric properties through experimental research and theoretical modeling

Lebda,

Atyia,

Habashy

2024

J Mater Sci: Mater Electron

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This study discusses the experimental findings on the frequency & temperature influences on the dielectric (constant (ε1) and loss (ε2)) of some chalcogenide materials based on Se83Bi17 composition performed in the temperature range 303 K–393 K and frequency range (100–1000000 Hz). As the frequency increases, multiple polarization mechanisms contribute to the reduction of the dielectric constant. The addition of germanium (Ge) to a composition increases ε1 more than tellurium (Te). The dielectric loss decreases with frequency while increasing with temperature and AC conductivity. Understanding these behaviors is important for material characterization and applications in fields like electronics and solar cells. The theoretical section introduces adaptive neuro-fuzzy inference systems (ANFIS), which are utilized in the estimation of the dielectric characteristics of Se83Bi17 (SB), Se83Bi17Te5 (SB-T), and Se83Bi17Ge5 (SB-G). Experimentation-related data are a source of input. ANFIS model of the Takagi–Sugeno type has been trained. With MATLAB, the most effective networks are created. The outcomes of the ANFIS modeling are exceptional. The accuracy of the modeling process is due to the error values. This study demonstrates that the ANFIS technique can accurately anticipate the dielectric properties of the compositions under consideration when they are formed into thin films. The ANFIS can describe the experimental data of the dielectric (constant (ε1) and loss (ε2)) of some chalcogenide materials for all the mentioned temperatures and frequencies. This leads to using the ANFIS model to produce the dielectric (constant (ε1) and loss (ε2)) of some chalcogenide materials for various temperatures and frequencies which there are no experimental data yet to compare with.

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Neuro-fuzzy modeling of deformation parameters for fusion-barriers

Cited by 7 publications

References 27 publications

Artificial Intelligence Approaches for Studying the pp Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Artificial Intelligence Approaches for Studying the pp Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System

Inference and visualization of nuclear magnetic moment studies with neuro-fuzzy systems

From experimentation to prediction: comprehensive study of dielectric properties through experimental research and theoretical modeling

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