The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

Xiao, Chuncai; Hao, Kuangrong

doi:10.3390/ma8010117

Cited by 12 publications

(9 citation statements)

References 34 publications

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“…Taking the RBF function as the kernel function, we demonstrate the flowchart of the PSO-SVM algorithm in Figure 3 . To make this algorithm clearer, the details of this algorithm is briefly introduced in Table 1 as follows [ 22 , 49 ]:…”

Section: Related Techniquesmentioning

confidence: 99%

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Wang

Niu

et al. 2016

IJERPH

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In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

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Section: Related Techniquesmentioning

confidence: 99%

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Wang

Niu

et al. 2016

IJERPH

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“…Support vector machine (SVM) is one of the machine learning-based classification algorithms. It has fewer parameters to adjust than other classification algorithms and can classify data with high accuracy even when the training dataset is small [ 22 ]. In this study, an impact damage determination model based on SVM was constructed by training the model with PVDF signal features according to the damage state of the impacted specimens.…”

Section: Introductionmentioning

confidence: 99%

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Won

Woo

et al. 2020

Materials

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Carbon fiber reinforced plastics (CFRPs) have high specific stiffness and strength, but they are vulnerable to transverse loading, especially low-velocity impact loadings. The impact damage may cause serious strength reduction in CFRP structure, but the damage in a CFRP is mainly internal and microscopic, that it is barely visible. Therefore, this study proposes a method of determining impact damage in CFRP via poly(vinylidene fluoride) (PVDF) sensor, which is convenient and has high mechanical and electrical performance. In total, 114 drop impact tests were performed to investigate on impact responses and PVDF signals due to impacts. The test results were analyzed to determine the damage of specimens and signal features, which are relevant to failure mechanisms were extracted from PVDF signals by means of discrete wavelet transform (DWT). Support vector machine (SVM) was used for optimal classification of damage state, and the model using radial basis function (RBF) kernel showed the best performance. The model was validated through a 4-fold cross-validation, and the accuracy was reported to be 92.30%. In conclusion, impact damage in CFRP structures can be effectively determined using the spectral analysis and the machine learning-based classification on PVDF signals.

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“…Benchmarking studies have shown that the SVM performs the best among current classification techniques [15]. Numerous experiments have shown that Support Vector Machine has satisfactory classification accuracies under a limited number of training samples, and it has been widely used in classification and prediction [9][10][11][12]16,17]. However, there is still a problem that the proper selection of kernel function and its parameters has great influence on the final prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

Jinglin

Liu

et al. 2017

Algorithms

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Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. To improve the prediction accuracy of precipitation, this context proposes a prediction model for rainfall forecast based on Support Vector Machine with Particle Swarm Optimization (PSO-SVM) to replace the linear threshold used in traditional precipitation. Parameter selection has a critical impact on the predictive accuracy of SVM, and PSO is proposed to find the optimal parameters for SVM. The PSO-SVM algorithm was used for the training of a model by using the historical data for precipitation prediction, which can be useful information and used by people of all walks of life in making wise and intelligent decisions. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm has much better accuracy than the direct prediction model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the SVM-PSO model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.

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The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

Cited by 12 publications

References 34 publications

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

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