The Investigation of Electron-Optical Parameters Using Artificial Neural Networks

Işık, Ali Hakan

doi:10.12693/aphyspola.127.1317

Cited by 8 publications

(7 citation statements)

References 20 publications

(10 reference statements)

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“…Artificial neural networks are also widely used in filter design [18]. It is also possible to use artificial neural networks in different signal processing tasks: investigation of electron optical parameters [19], analysis of radial dependence of the localized magnetic field [20], classification of electron gun operation modes [21]. Artificial neural networks can be also used in combination with other techniques.…”

Section: Introductionmentioning

confidence: 99%

Delay Systems Synthesis using Multi-Layer Perceptron Network

et al. 2018

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The aim of this paper is to accelerate development and investigation of the delay systems. The computational time for investigation of particular design of delay system may take from several minutes up to several days. To achieve the required constructional parameters of the system, the iterative calculations usually should be repeated many times. In this paper, an artificial neural network is proposed to be used as the universal approximator for solving mathematical problems of delay system investigation instead of usual analytical and numerical techniques. The application of a multi-layer perceptron is proposed for approximation of solution space with discrete estimates, which were initially received by application of numerical techniques. Different structures of the multi-layer perceptron were tested for approximation. The difference between delay systems synthesis, which was estimated using numerical techniques and trained multi-layer perceptron did not exceed 5% for any of the six design parameter values. The execution time for estimating single delay system was reduced from 240 s to 20 ms. Such fast estimation of design parameters enables performing delay system analysis and design in real time, preserving time for structure visualization in 3D or virtual reality environment.

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Section: Introductionmentioning

confidence: 99%

Delay Systems Synthesis using Multi-Layer Perceptron Network

et al. 2018

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“…In recent years, artificial neural networks have seen an explosion of interest in atomic and molecular physics [12][13][14][15][16]. Artificial neural networks (ANNs) are parallel computing systems which are modelled on the behavior of neurons in the human brain.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Radial Dependence of the Localized Magnetic Field using Artificial Neural Networks

Yang

Tang²,

Gao

2017

Acta Phys. Pol. A

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The measurements of the angular distributions of charged particles have a long history in atomic and molecular collision studies. To detect all electrons originating from collision has great importance in experimental studies. Due to the physical constraints of the experimental instruments, electrons in definite angles can be detected. Magnetic angle changer is designed to steer electrons scattered at undetectable angles. The magnetic angle changer is a source of the localized magnetic field. A well-controlled magnetic field in the interaction region changes the angles of the electron trajectories. In this study, artificial neural networks have been performed to obtain variation of the magnetic field strength as a function of radial distance calculated from boundary element method. A stringent quality filter is used for data to produce more robust artificial neural network based prediction. The results indicate that the well-trained artificial neural networks can predict the effect on the radial dependence of the localized magnetic field with tremendous precision. It is believed that this study will introduce a new insight into collision studies.

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“…In the absence of any fringing fields, the ideal HDAs indicate the first order focusing characteristics. In recent years, successful applications are performed for solving problems in charged particle optics using artificial neural networks (ANNs) in prediction [14,15] and classification [16][17][18]. ANNs are parallel computing systems with interconnected processors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of First Order Focusing Properties of Ideal Hemispherical Deflector Analyzer Using Artificial Neural Network

et al. 2017

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Electrostatic energy analyzers are irreplaceable instruments to analyze the electron beams energies. In this context, the knowledge of electron trajectories in electrostatic energy analyzers has major importance in collision physics as well as in different scientific instruments for surface science. In this study, electron trajectories for different energies in an ideal field 180• hemispherical deflector analyzer are investigated by artificial neural network prediction method. The SIMION 8.1 simulation program is used as a data source for training and testing of artificial neural network. Artificial neural network based prediction has been performed using Matlab R2012b program. Obtained performance results indicate that this approach provides new perspectives for the rapid solution to the problems in charged particle optics.

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The Investigation of Electron-Optical Parameters Using Artificial Neural Networks

Cited by 8 publications

References 20 publications

Delay Systems Synthesis using Multi-Layer Perceptron Network

Delay Systems Synthesis using Multi-Layer Perceptron Network

Analysis of Radial Dependence of the Localized Magnetic Field using Artificial Neural Networks

Prediction of First Order Focusing Properties of Ideal Hemispherical Deflector Analyzer Using Artificial Neural Network

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