Prediction of fatigue life of packaging EMC material based on RBF-SVM

Guo, Hai; Yin, Jinghua; Zhao, Jiewen; Huang, Zhiyu; Pan, Y.

doi:10.1504/ijmpt.2014.062934

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…Comparison results of the prediction performance of these models are shown in Table 4. The correlation coefficient of SGBS model is 0.962, larger than that of the models of linear regression [23], BP neural network [24], GRNN neural network [7], SMO-SVR [25], and SVR [26], proving that the linear regression relationship of the SGBS model is better than that of the other five models. In the SGBS model, the root mean squared error and root mean squared error are 20.8668 and 26.520%, respectively, lower than those of the other models, certifying its better prediction performance.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films

Guo

Yin

Zhao

et al. 2015

Journal of Nanomaterials

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Using the method of Stochastic Gradient Boosting, ten SMO-SVR are constructed into a strong prediction model (SGBS model) that is efficient in predicting the breakdown field strength. Adopting the method of in situ polymerization, thirty-two samples of nanocomposite films with different percentage compositions, components, and thicknesses are prepared. Then, the breakdown field strength is tested by using voltage test equipment. From the test results, the correlation coefficient (CC), the mean absolute error (MAE), the root mean squared error (RMSE), the relative absolute error (RAE), and the root relative squared error (RRSE) are 0.9664, 14.2598, 19.684, 22.26%, and 25.01% with SGBS model. The result indicates that the predicted values fit well with the measured ones. Comparisons between models such as linear regression, BP, GRNN, SVR, and SMO-SVR have also been made under the same conditions. They show that CC of the SGBS model is higher than those of other models. Nevertheless, the MAE, RMSE, RAE, and RRSE of the SGBS model are lower than those of other models. This demonstrates that the SGBS model is better than other models in predicting the breakdown field strength of polyimide nanocomposite films.

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Section: Experimental Results and Analysismentioning

confidence: 99%

An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films

Guo

Yin

Zhao

et al. 2015

Journal of Nanomaterials

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“…Thus it has been proved that classification effects of class 3 by this model are the best, while those of class 1 and class 2 are lower. In experiment 2, the GABD method has been compared, under the same sample database, to the support vector machine (SVM) [12], BayesNet [13], multilayer perceptron (MLP) neural network [14], RBF neural network [15], and BP neural network [16] models, respectively, in order to further check the detection to the surface texture of polyimide matrix's inorganic nanocomposite thin film. The comparison results of the classification performances of detection models are obtained, as shown in Table 4 and Figure 6.…”

Section: Results and Analysismentioning

confidence: 99%

“…(3) Through the comparison experiment we know that the classification performance of GABD is better than that of SVM [12], BayesNet [13], multilayer perceptron (MLP) neural network [14], RBF neural network [15], and BP neural network [16] models under the same sample database. Moreover, it is superior to single classifiers with respect to the accuracy of nanocomposite film detection.…”

Section: Resultsmentioning

confidence: 99%

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

Guo

Yin

Zhao

et al. 2015

Advances in Materials Science and Engineering

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An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine), NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.

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“…Zhang et al demonstrated good fitting accuracy by utilizing an SVM model optimized by a genetic algorithm to model the fatigue life of materials. A fatigue prediction model of elastic memory composites materials was developed using an RBF SVM by Guo et al A. Hemmati‐Sarapardeh et al used four different machine learning methods to estimate the physical properties of pure substances . A computational intelligence (CI)‐based load forecasting technique and its optimizations with both heuristic and meta‐heuristic methods were reviewed systematically by S.N.…”

Section: Introductionmentioning

confidence: 99%

“…Tao et al 12 15 A. Hemmati-Sarapardeh et al used four different machine learning methods to estimate the physical properties of pure substances. 16 A computational intelligence (CI)-based load forecasting technique and its optimizations with both heuristic and meta-heuristic methods were reviewed systematically by S.N.…”

Section: Introductionmentioning

confidence: 99%

Fatigue life prediction for vibration isolation rubber based on parameter‐optimized support vector machine model

Liu

Shi

Chen

2018

Fatigue Fract Eng Mat Struct

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Given the small sample size, nonlinearity, and large dispersion of the measured data of fatigue performance for vibration isolation rubbers, the fatigue life prediction model for vibration isolation rubber materials was established using a support vector machine (SVM). A modified gravity search algorithm (MGSA) is proposed to optimize the parameters of the SVM. Using environmental temperature, the Rockwell hardness of the rubber compound, and the engineering strain peak as the input variables, the model was trained based on the experimental fatigue data of vibration isolation rubber materials. For comparison, the standard genetic algorithm, the standard particle swarm algorithm, and the standard simulated annealing algorithm are also implemented. Moreover, a back propagation neural network regression model is applied to the life prediction, with the conclusion that the prediction accuracy and the efficiency of MGSA are better than those of extant methods. This work can provide reference for further fatigue life prediction and structural improvement of rubber parts.

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Prediction of fatigue life of packaging EMC material based on RBF-SVM

Cited by 11 publications

References 10 publications

An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films

An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

Fatigue life prediction for vibration isolation rubber based on parameter‐optimized support vector machine model

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