scholarly journals Multipoint Vibration Response Prediction under Uncorrelated Multiple Sources Load Based on Elastic-Net Regularization in Frequency Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
ZhenKai Cui ◽  
Cheng Wang ◽  
Jianwei Chen ◽  
Ting He
Keyword(s):  
Linear Regression ◽  
Frequency Domain ◽  
Response Prediction ◽  
Acoustic Vibration ◽  
Vibration Response ◽  
Elastic Net ◽  
Multiple Sources ◽  
Regression Methods ◽  
Elastic Net Regularization ◽  
Working Situation

In order to solve the problems of large number of conditions at inherent frequencies and low prediction accuracy when using multiple multivariate linear regression methods for vibration response prediction alone, an elastic-net regularization method is proposed. Firstly, a multi-input and multioutput linear regression model of the multipoint frequency domain vibration response is trained using historical data at each frequency point. Secondly, the trained model under each frequency point is improved by the elastic regularization. Finally, the model is used in a working situation. The predicted vibration response on the experimental dataset of cylindrical shell acoustic vibration showed that the improvement of the multivariate regression vibration response prediction model by elastic regularization can better improve the accuracy and reduce the large number of conditions at some frequencies.

Elastic-net regularization based multi-point vibration response prediction in situation of unknown uncorrelated multiple sources load

2020 ◽  
Vol 64 (1-4) ◽  
pp. 649-657
Author(s):  
Delei Chen ◽  
Cheng Wang ◽  
Xiongming Lai ◽  
Huizhen Zhang ◽  
Haibo Li ◽  
...  
Keyword(s):  
Transfer Function ◽  
Prediction Method ◽  
Response Prediction ◽  
Ordinary Least Squares ◽  
Vibration Response ◽  
Elastic Net ◽  
Multiple Sources ◽  
Elastic Net Regularization ◽  
Regularization Parameters ◽  
Ill Posed

In order to reduce the influence of ill-posed inverse on response prediction in the situation of unknown uncorrelated multiple sources load, a response prediction method based on elastic-net regularization in the frequency domain was proposed. This method utilized the linear relationship between known responses and the unknown responses instead of the transfer function to predict the response. Moreover, the elastic-net regularization model has two regularization parameters combining l1, l2 regularization to reduce the influence of ill-posed inverse. The experiment results on the data of acoustic and vibration sources on cylindrical shells showed that the elastic-net regularization in predicting response could obtain higher accurate results compared with the method of transfer function and the method of ordinary least squares, and predict vibration response effectively and satisfy industrial requirements.

scholarly journals MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain

2020 ◽  
Vol 10 (24) ◽  
pp. 8784
Author(s):  
Cheng Wang ◽  
Delei Chen ◽  
Haiyang Huang ◽  
Wei Zhan ◽  
Xiongming Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Linear Regression ◽  
Frequency Domain ◽  
Matrix Method ◽  
Response Prediction ◽  
Vibration Response ◽  
Data Driven ◽  
Training Dataset ◽  
Network Method

To predict the multi-point vibration response in the frequency domain when the uncorrelated multi-source loads are unknown, a data-driven and multi-input multi-output least squares support vector regression (MIMO LS-SVR)-based method in the frequency domain is proposed. Firstly, the relationship between the measured multi-point vibration response and unmeasured multi-point vibration response is formulated using the transfer function in the frequency domain. Secondly, the data-driven multiple regression analysis problem of multi-point vibration response prediction in the frequency domain is described formally, and its mathematical model is established. With the measured multi-point vibration response as the input and the unmeasured multi-point vibration response as the output, the vibration response history data are assembled as a MIMO training dataset at each frequency. Thirdly, using the MIMO LS-SVR algorithm and MIMO history training dataset, the multi-point vibration response prediction model is built at each frequency point. By comparing the transmissibility matrix method, multiple linear regression model-based method, and MIMO neural network method, the application scope of the proposed method and its advantages are analyzed. The experimental results for acoustic and vibration experiment on a cylindrical shell verified that the MIMO LS-SVR-based method predicts the multi-point vibration response effectively when the loads are unknown, and has higher precision than the transfer function method, multiple linear regression method, MIMO neural network method, and transmissibility matrix method.

scholarly journals Performance analysis on least absolute shrinkage selection operator, elastic net and correlation adjusted elastic net regression methods

2015 ◽  
Vol 3 (1) ◽  
pp. 93
Author(s):  
Pascalis Kadaro Matthew ◽  
Abubakar Yahaya
Keyword(s):  
Linear Regression ◽  
Prediction Accuracy ◽  
Penalized Regression ◽  
Ordinary Least Squares ◽  
Complex Model ◽  
Elastic Net ◽  
Data Set ◽  
Regression Methods ◽  
Regression Techniques ◽  
Selection Operator

<p>Some few decades ago, penalized regression techniques for linear regression have been developed specifically to reduce the flaws inherent in the prediction accuracy of the classical ordinary least squares (OLS) regression technique. In this paper, we used a diabetes data set obtained from previous literature to compare three of these well-known techniques, namely: Least Absolute Shrinkage Selection Operator (LASSO), Elastic Net and Correlation Adjusted Elastic Net (CAEN). After thorough analysis, it was observed that CAEN generated a less complex model.</p>

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

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