Robust support vector regression with generic quadratic nonconvex ε-insensitive loss

Ye, Yafen; Gao, Junbin; Shao, Yuan‐Hai; Li, Chunna; Jin, Yan; Hua, Xiang‐Yu

doi:10.1016/j.apm.2020.01.053

Cited by 30 publications

(8 citation statements)

References 26 publications

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“…Support vector regression (SVR), as an extension of the support vector machines in regressions, is commonly applied in a variety of fields [ 42 ]. BPNN, as a known machine learning method, is proven to have great potential in rapid detection of meat adulteration [ 14 , 20 ].…”

Section: Resultsmentioning

confidence: 99%

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Huang

2022

Foods

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Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.

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Section: Resultsmentioning

confidence: 99%

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Huang

2022

Foods

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“…For regression problems, we give a data set D = f(x 1 , y 1 ), (x 2 , y 2 ), :::(x m , y m )g, The regression model we expected is f (x) = w T x + b, where w T and b are model parameters. To make f (x) and y m as close as possible, the SVR problem can be written as follows (Ye et al, 2020):…”

Section: Black Box Model Of Solenoid Valve Based On Support Vector Regressionmentioning

confidence: 99%

Experimental and numerical validation of a proportional solenoid valve based on the data-driven model

Jiang

Ren

et al. 2021

Transactions of the Institute of Measurement and Control

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Solenoid valves are widely used in mechatronics, robotic systems and industrial occasions. An accurate model is very important for the design and control of a solenoid valve. The dynamical model of the solenoid valve is difficult to obtain due to the complexity of the structure and the interaction of multiple physical fields. This paper proposes two kinds of model of solenoid valve: grey box model and black box model, on the basis of experimental data. ARX model is selected as the basic structure of the grey box model. After clustering the data with the fuzzy c-means algorithm, the overall experimental data is divided into several local linear sub-models, and the model coefficients of the local linear model are obtained by partial least square regression. The overall expression of the model is obtained by combining the local sub-models with membership degree. For the black box model, support vector regression algorithm is used to identify. On the basis of selecting the appropriate parameters, we obtain the black box model of solenoid valve based on data. For the above two models, we carry out experimental verification and error analysis, and compare with the traditional modelling method. According to the results, it can be seen that on the basis of the experimental data, using the data-driven method to construct the model has many advantages, avoiding complex physical analysis, and has high accuracy. The model with high precision will be used in the accurate control and observing estimation of the solenoid valve.

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“…erefore, the loss function of traditional SVM needs to be embedded in the distribution of data uncertainty, which can ensure the utilization of observation data quality monitoring [20,21].…”

Section: Introductionmentioning

confidence: 99%

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

Tao

2022

Mathematical Problems in Engineering

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The loss function of the traditional support vector machine (SVM) method consists of hinge function and regularization, which is difficult to achieve the quality control of observation data. It requires a new loss function to measure the quality of the observed data. At this stage, researchers will use data cleaning or data preprocessing to process observational data. The data preprocessing method will normalize the data features, which will make the data process the same interval and a distribution. However, this method is also difficult to guarantee the accuracy of the data. This study uses the SVM method to study the integrity, unity, and accuracy of the observation data. This study also proposes a new loss function, which can obtain the uncertainty distribution of the observed data. The loss function of traditional SVM methods is that the uncertainty distribution of the data cannot be obtained. The results show that the SVM algorithm with the new loss function has better accuracy in processing the observed data. For the maximum prediction error of the observed data, the error with the new loss function is only 2.58%, which is reduced by 0.21% compared to the SVM method with the old loss function. However, the prediction error of the SVM algorithm with the old loss function is 2.78%, and the SVM algorithm with the new loss function has obvious prediction accuracy.

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Robust support vector regression with generic quadratic nonconvex ε-insensitive loss

Cited by 30 publications

References 26 publications

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Experimental and numerical validation of a proportional solenoid valve based on the data-driven model

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

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