An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Bi, Kexin; Qiu, Tong

doi:10.1016/j.cjche.2018.12.015

Cited by 39 publications

(20 citation statements)

References 13 publications

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“…Therefore, it is a great strategy to solve parameter optimization by combining multiple optimization ideas. Bi and Qiu [ 53 ] combined Genetic Algorithm (GA) and SA algorithm to propose an effective global optimization algorithm, and experiments showed that the convergence speed of the algorithm was improved. Mafarja and Mirjalili [ 54 ] proposed two hybrid schemes based on SA and WOA for the feature selection problem, one is to embed SA into WOA to enhance the search ability of the population, and the other is to use SA algorithm to further search for the best solution after the solution of WOA algorithm, and the experiments verified that the hybrid approach can improve the classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Chang

Zheng

2021

Computational Intelligence and Neuroscience

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Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model’s training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.

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Section: Related Workmentioning

confidence: 99%

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Chang

Zheng

2021

Computational Intelligence and Neuroscience

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“…The optimization process is time-consuming and sensitive to population initialization. The combined use of GA and PSO algorithms improves the shortcoming that easily falls into the local extreme value and slow operation speed of the GA algorithm and improves the operation accuracy of the PSO algorithm (Bi and Qiu, 2019;Chen and Li, 2019).…”

Section: Validation Of Mre Constitutive Model Parameter Identification Optimization Algorithmmentioning

confidence: 99%

Modified Bouc-Wen Model Based on Fractional Derivative and Application in Magnetorheological Elastomer

et al. 2021

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To accurately characterize the mechanical behavior of magnetorheological elastomer (MRE) under a wide range of strain amplitude, excitation frequency, and magnetic field, the viscoelastic fractional derivative was introduced, and a modified Bouc-Wen model based on fractional derivative for MRE in a nonlinear viscoelastic region was established. The Bouc-Wen model can accurately describe the hysteretic characteristics of the MRE nonlinear viscoelastic region, but it cannot accurately simulate magneto-viscoelasticity and frequency dependence. The fractional derivative can express this characteristic with fewer parameters and higher accuracy. The model’s validity was verified by fitting the experimental data of stress and strain measured in shear mode. By analyzing the coupling relationship between the model parameters and strain amplitude, frequency, and magnetic flux densities, a method of parameter identification under multi-loading conditions was proposed, and the modified model parameters were identified. The results reveal that the modified Bouc-Wen model can accurately characterize the mechanical properties of the nonlinear viscoelastic region of MRE, and the fitting accuracy is significantly improved compared with the Bouc-Wen model. The expression of the model parameters obtained from the method of parameter identification under multi-loading conditions is accurate in a wide range of strain amplitude, frequency, and magnetic flux density. The fitness values of simulation data and experimental data under identified and non-identified conditions exceed 90%, showcasing the effectiveness of the modified Bouc-Wen model and the feasibility of the parameter identification method under multi-loading conditions.

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“…Choosing different kernel functions will lead to great differences in classification accuracy. At present, the kernel functions commonly used in SVM models include linearity, polynomial, and radial basis function (RBF) [16]. Since the classification accuracy of the RBF kernel function is much higher than that of other kernel functions and is suitable for situations where the number of features is less than or equal to the number of samples [17,18], this paper chooses the RBF kernel function, as shown in the following equation:…”

Section: Data Block Update Integration Algorithmmentioning

confidence: 99%

Network Pseudohealth Information Recognition Model: An Integrated Architecture of Latent Dirichlet Allocation and Data Block Update

et al. 2020

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The wanton dissemination of network pseudohealth information has brought great harm to people’s health, life, and property. It is important to detect and identify network pseudohealth information. Based on this, this paper defines the concepts of pseudohealth information, data block, and data block integration, designs an architecture that combines the latent Dirichlet allocation (LDA) algorithm and data block update integration, and proposes the combination algorithm model. In addition, crawler technology is used to crawl the pseudohealth information transmitted on the Sina Weibo platform during the “epidemic situation” from February to March 2020 for the simulation test on the experimental case dataset. The research results show that (1) the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; (2) the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and (3) considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine (SVM) model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model.

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An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Cited by 39 publications

References 13 publications

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Modified Bouc-Wen Model Based on Fractional Derivative and Application in Magnetorheological Elastomer

Network Pseudohealth Information Recognition Model: An Integrated Architecture of Latent Dirichlet Allocation and Data Block Update

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