scholarly journals Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 7902
Author(s):  
Wei-Cheng Shih ◽  
Furqan Furqanuddin ◽  
Po-Lin Lee ◽  
Jui-Pin Hung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Contact Stiffness ◽  
Feeding Rates ◽  
Linear Guide ◽  
Average Percentage ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship

In this paper, we propose an artificial neural network (ANN) predictive model to identify the linear guide preload based on the measured vibration features of the feeding stage. In this study, the relationship between the contact stiffness and preload level of a linear guide was investigated by an experimental analysis. Furthermore, the stage was assembled with different linear guide preloads for the motion test to assess the vibrations. Vibration levels with changes in preload values and feeding rates were examined. The predictive models were established and verified based on a dataset collected from tests using an ANN approach. The ANN models were shown to have an excellent accuracy of 96.5% in the training datasets, which were collected from stages with sliding blocks rated at consistent preloads. The average percentage prediction error in the verification dataset was approximately 8.54–11.23%. This is probably because the stage with an unevenly distributed preload in the sliding blocks induces vibration with more fluctuation, which eventually affects the prediction accuracy. The results verify the feasibility of online preload identification for the condition monitoring of the feeding system.

scholarly journals Solving multiple linear regression problem using artificial neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 770
Author(s):  
Mohammad S. Khrisat ◽  
Ziad A. Alqadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Problem ◽  
Simple Method ◽  
Scientific Experiments ◽  
Linear Regression Problem ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship ◽  
Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

scholarly journals An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

2013 ◽  
Vol 64 (5) ◽  
pp. 317-322 ◽  
Author(s):  
Ali Akdagli ◽  
Abdurrahim Toktas ◽  
Ahmet Kayabasi ◽  
Ibrahim Develi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Multilayer Perceptrons ◽  
Ann Model ◽  
Resonant Frequencies ◽  
Average Percentage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

scholarly journals Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Pei-Fang (Jennifer) Tsai ◽  
Po-Chia Chen ◽  
Yen-You Chen ◽  
Hao-Yuan Song ◽  
Hsiu-Mei Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coronary Atherosclerosis ◽  
Inpatient Care ◽  
Length Of Hospital Stay ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Christian Hospital

For hospitals’ admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.

scholarly journals A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Agus Saptoro ◽  
Moses O. Tadé ◽  
Hari Vuthaluru
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Data Representation ◽  
Data Splitting ◽  
Neural Network Models ◽  
Data Division ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.

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