scholarly journals Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2570
Author(s):  
Tomasz Trzepieciński ◽  
Marcin Szpunar ◽  
Ľuboš Kaščák
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Variable Load ◽  
Training Methods ◽  
Backward Elimination ◽  
Drawing Test ◽  
Strip Drawing ◽  
Rms Error ◽  
Artificial Neural ◽  
Quasi Newton

This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (R2 = 0.91) was observed for a multi-layer network with eight neurons in the hidden layer trained using the quasi-Newton algorithm. As a result of the non-linear relationship between clamping and friction force, the value of the COF decreased with increasing load. The regression model F-value of 22.13 implies that the model with R2 = 0.6975 is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

Learning in Artificial Neural Networks: A Statistical Perspective

1989 ◽  
Vol 1 (4) ◽  
pp. 425-464 ◽  
Author(s):  
Halbert White
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Theory ◽  
Comprehensive Treatment ◽  
Training Methods ◽  
Network Learning ◽  
Advantages And Disadvantages ◽  
Systems Identification ◽  
Artificial Neural ◽  
Insight Into

The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.

scholarly journals Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5542
Author(s):  
Alejandro Grande-Fidalgo ◽  
Javier Calpe ◽  
Mónica Redón ◽  
Carlos Millán-Navarro ◽  
Emilio Soria-Olivas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reconstruction Algorithm ◽  
High Accuracy ◽  
Recording Device ◽  
Lead System ◽  
Ambulatory Ecg ◽  
Rms Error ◽  
Artificial Neural ◽  
Electrocardiogram Ecg

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 μV and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.

scholarly journals Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy

Coatings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Marek Gąsiorowski ◽  
Piotr Szymak ◽  
Leszek Bychto ◽  
Aleksy Patryn
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Reflectance Spectra ◽  
Training Methods ◽  
Optical Reflectance ◽  
Research Cycle ◽  
Evaluation Module ◽  
Artificial Neural

This article undertakes the subject matter of applying artificial neural networks to analyze optical reflectance spectra of objects exhibiting a change of optical properties in the domain of time. A compact Digital Light Projection NIRscan Nano Evaluation Module spectrometer was used to record spectra. Due to the miniature spectrometer’s size and its simplicity of measurement, it can be used to conduct tests outside of a laboratory. A series of plant-derived objects were used as test subjects with rapidly changing optical properties in the presented research cycle. The application of artificial neural networks made it possible to determine the aging time of plants with a relatively low mean squared error, reaching 0.56 h for the Levenberg–Marquardt backpropagation training method. The results of the other ten training methods for artificial neural networks have been included in the paper.

scholarly journals (Application of Artificial Neural Networks Model for Forecasting Consumption of Electricity in Gezira State, Sudan (2006-2018: تطبيقات الشبكات العصبية الاصطناعية للتنبؤ باستهلاك الكهرباء في ولاية الجزيرة، السودان (2006-2018)

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Nada Mohammed Ahmed Alamin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Time Series Data ◽  
Electricity Consumption ◽  
Series Data ◽  
Control Center ◽  
Consumption Data ◽  
Artificial Neural ◽  
Quasi Newton ◽  
National Control

This paper aimed applying models of artificial neural networks to electricity consumption data in the Gezira state, Sudan for the period (Jan 2006- May 2018), and predicting future values for the period (Jun 2018- Dec 2020) by train a recurrent neural network using Quasi-Newton Sampling and using online learning. The study relied on data from the national control center. After applying artificial neural networks, The Thiel coefficient is used to confirm the efficiency of the model, and the paper recommends the use of artificial neural networks to various time series data due to their strength and Accuracy.

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