Online learning method based on artificial neural network to optimize magnetic shielding characteristic parameters

This study aims to simulate performance deterioration of a microturbine and apply artificial neural network to its performance diagnosis. As it is hard to obtain test data with degraded component performance, the degraded engine data have been acquired through simulation. Artificial neural network is adopted as the diagnosis tool. First, the microturbine has been tested to get reference operation data, assumed to be degradation free. Then, a simulation program was set up to regenerate the performance test data. Deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (deterioration of single and two components) were simulated to generate fault data. The neural network was trained with majority of the data sets. Then, the remaining data sets were used to check the predictability of the neural network. Given measurable performance parameters (power, temperatures, pressures) as inputs to the neural network, characteristic parameters of each component were predicted as outputs and compared with original data. The neural network produced sufficiently accurate prediction. Reducing the number of input data decreased prediction accuracy. However, excluding up to a couple of input data still produced acceptable accuracy.

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An Artificial Neural Network Based Learning Method for Mobile Robot Localization

Robotics Automation and Control ◽

10.5772/5830 ◽

2008 ◽

Cited By ~ 5

Author(s):

Matthew Conforth ◽

Yan Meng

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mobile Robot ◽

Robot Localization ◽

Learning Method ◽

Mobile Robot Localization ◽

Artificial Neural

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Prediction of Static Characteristic Parameters of an Insulated Gate Bipolar Transistor Using Artificial Neural Network

Micromachines ◽

10.3390/mi13010004 ◽

2021 ◽

Vol 13 (1) ◽

pp. 4

Author(s):

Qing Yao ◽

Yufeng Guo ◽

Bo Zhang ◽

Jing Chen ◽

Jun Zhang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Computer Aided Design ◽

Structural Parameters ◽

Characteristic Parameter ◽

Static Characteristic ◽

Characteristic Parameters ◽

Tcad Simulation ◽

Artificial Neural ◽

Latch Up

Breakdown voltage (BV), on-state voltage (Von), static latch-up voltage (Vlu), static latch-up current density (Jlu), and threshold voltage (Vth), etc., are critical static characteristic parameters of an IGBT for researchers. Von and Vth can characterize the conduction capability of the device, while BV, Vlu, and Jlu can help designers analyze the safe operating area (SOA) of the device and its reliability. In this paper, we propose a multi-layer artificial neural network (ANN) framework to predict these characteristic parameters. The proposed scheme can accurately fit the relationship between structural parameters and static characteristic parameters. Given the structural parameters of the device, characteristic parameters can be generated accurately and efficiently. Compared with technology computer-aided design (TCAD) simulation, the average errors of our scheme for each characteristic parameter are within 8%, especially for BV and Vth, while the errors are controlled within 1%, and the evaluation speed is improved more than 107 times. In addition, since the prediction process is mathematically a matrix operation process, there is no convergence problem, which there is in a TCAD simulation.

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Recognition of Beam's Music Notation Patterns Using Artificial Neural Networks with The Backpropagation Method

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v3i1.2557 ◽

2019 ◽

Vol 3 (1) ◽

pp. 41

Author(s):

John Pierre Haumahu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

System Development ◽

Musical Instruments ◽

Music Notation ◽

Learning Method ◽

Backpropagation Method ◽

Artificial Neural ◽

Numerical Notation

<p class="8AbstrakBahasaIndonesia"><span>The beam notations is officially used as the standard of international music notation, and is often found in scores for both musical instruments and vocals. In Indonesia, the use of numerical notation is more widely used and understood, because the learning process of notation beams is not easy, and takes time for the introduction of each symbol and its meaning. The pattern recognition technology makes it possible to recognize the pattern of the beam notations. The software used for system development is Matlab, utilizing artificial neural network using backpropagation method to recognize the pattern of beam notation. Backpropagation is a supervised learning method, where the system will be given the training first, and then the system can understand and identify patterns based on the knowledge gained. The final result shows that the system is able to recognize patterns from notations that have been previously studied with the highest percentage of 91.20%.</span></p>

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Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar

IJAIT (International Journal of Applied Information Technology) ◽

10.25124/ijait.v4i02.3381 ◽

2021 ◽

pp. 81

Author(s):

Eko Verianto ◽

Budi Sutedjo Dharma Oetomo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Exchange Rate ◽

Pso Algorithm ◽

Learning Method ◽

Currency Exchange ◽

Currency Exchange Rate ◽

The Us ◽

Us Dollar ◽

Artificial Neural

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

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Intelligent Choice of Characteristic Parameters of Oilstone in Honing of Titanium Alloy Cylinder Based on Artificial Neural Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.426-427.35 ◽

2010 ◽

Vol 426-427 ◽

pp. 35-39 ◽

Cited By ~ 1

Author(s):

Yi Fang Wen ◽

Yan Nian Rui ◽

Jian Dong Cao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Titanium Alloy ◽

High Reliability ◽

Experimental Studies ◽

Surface Hardness ◽

Characteristic Parameters ◽

Workpiece Material ◽

Organizational Stability ◽

Artificial Neural

Titanium alloys have good mechanical properties and organizational stability. However, due to the larger viscousity of titanium, a reasonable choice of the characteristic parameters of oilstone will directly affect the quality and efficiency of honing processing. This article solved multi-objective problem using artificial neural network with fast convergence and high precision. Based on a comprehensive analysis of the relationship between the workpiece material, materials status, surface hardness, the required surface quality and various parameters of oilstone, the improved artificial neural network algorithm-GCAQBP was adopted, through coding optimization of input and output parameters, model of intelligent choice of oilstone’s parameters was constructed about titanium alloy cylinder honing processing. Through experimental studies, it is shown that the intelligent model can choose quickly with high reliability compared with the traditional experience.

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