Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm

Jiang, Hui‐Qing; Yan, Zhengbing; Liu, Xinggao

doi:10.1016/j.neucom.2013.03.006

Cited by 33 publications

(36 citation statements)

References 33 publications

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“…iter max is the maximal number of iteration. The ICPSO algorithm can enhance the diversity in a colony and the reliability of global convergence, and it has the desirable characteristics in optimization and offers significant advantages over the simplex PSO algorithm [9].…”

Section: A Icpso Algorithmmentioning

confidence: 99%

“…Set the ICPSO parameters including the population size N, maximum number of iterations iter max , fitness of each particle according to eqn (9), weighted factor w k , and the range of the velocity between -v max to v max , where v max is a predefined boundary value according to the corresponding experiment data;…”

Section: Hybrid Icpso-wlssvm Alg$orithmmentioning

confidence: 99%

“…Step 2: Evaluate the fitness of particle according to eqn (9), and set the p best from the particle with the minimal fitness in the swarm. From the p best particles, we can get the initial g best ;…”

Section: Hybrid Icpso-wlssvm Alg$orithmmentioning

confidence: 99%

See 2 more Smart Citations

Elongation Prediction of Strip Steel in Annealing Furnace based on KPCA and Optimized LSSVM with ICPSO

Wang

Gu³

et al. 2015

Advances in Intelligent Systems Research

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-The elongation prediction of strips in furnace is extremely important in annealing process, which determines the quality and yield of product [1, 2]. Furthermore, the safety of airknife also depends on the prediction accuracy [3, 4]. Thus, the optimal soft-sensing method is proposed based on kernel principal component analysis (KPCA) and optimized weighted least squares support vector machine (WLSSVM) by immune clone particle swarm optimization (ICPSO). Avoiding the particles are easy to sink into premature convergence and run into local optimization in the iterative process by using ICPSO , which generated by particle swarm optimization (PSO) algorithm, and the ICPSO is also used to optimize the parameters of WLSSVM. Then, the method uses KPCA to denoise the input data set and capture the high-dimensional nonlinear principal components in input data space, and the principal components are input into the ICPSO-WLSSVM model to establish the softsensing prediction model. The proposed method is successfully applied in the strip elongation prediction in annealing furnace. The simulations show that the KPCA and ICPSO-WLSSVM model has higher prediction accuracy compared with other algorithms that verified with production data.

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Section: A Icpso Algorithmmentioning

confidence: 99%

Section: Hybrid Icpso-wlssvm Alg$orithmmentioning

confidence: 99%

See 1 more Smart Citation

Elongation Prediction of Strip Steel in Annealing Furnace based on KPCA and Optimized LSSVM with ICPSO

Wang

Gu³

et al. 2015

Advances in Intelligent Systems Research

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“…Regarding recent credit scoring techniques, artificial neural network [4,5] has been criticized for its poor performance when incorporating irrelevant attributes or small data sets, while support vector machine, motivated by statistical learning theory [6,7], is particularly well suited for coping with a large number of explanatory attributes or sparse data sets [8][9][10][11]. Baesens et al studied the performance of various state-of-the-art classification algorithms on eight real-life credit scoring data sets [12].…”

Section: Introductionmentioning

confidence: 99%

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

Shi

2016

JRFM

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Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.

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“…[27][28][29][30][31][32] To construct a high-precision LSSVM model, the parameters of LSSVM should be carefully set. [30][31][32] Appropriate parameters ensure the precision and efficiency of the structural reliability analysis. In this article, leave-one-out cross-validation (LOOCV) [33][34][35] is applied to seek the optimal parameters to improve the approximation capability and generalization ability of the LSSVM model, which makes the LSSVM model effectively overcome the appearance of over-learning and under-learning and greatly improves computational accuracy.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis on resonance for low-pressure compressor rotor blade based on least squares support vector machine with leave-one-out cross-validation

Gao

Bai

2015

Advances in Mechanical Engineering

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This research article analyzes the resonant reliability at the rotating speed of 6150.0 r/min for low-pressure compressor rotor blade. The aim is to improve the computational efficiency of reliability analysis. This study applies least squares support vector machine to predict the natural frequencies of the low-pressure compressor rotor blade considered. To build a more stable and reliable least squares support vector machine model, leave-one-out cross-validation is introduced to search for the optimal parameters of least squares support vector machine. Least squares support vector machine with leave-one-out cross-validation is presented to analyze the resonant reliability. Additionally, the modal analysis at the rotating speed of 6150.0 r/min for the rotor blade is considered as a tandem system to simplify the analysis and design process, and the randomness of influence factors on frequencies, such as material properties, structural dimension, and operating condition, is taken into consideration. Back-propagation neural network is compared to verify the proposed approach based on the same training and testing sets as least squares support vector machine with leave-one-out crossvalidation. Finally, the statistical results prove that the proposed approach is considered to be effective and feasible and can be applied to structural reliability analysis.

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Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm

Cited by 33 publications

References 33 publications

Elongation Prediction of Strip Steel in Annealing Furnace based on KPCA and Optimized LSSVM with ICPSO

Elongation Prediction of Strip Steel in Annealing Furnace based on KPCA and Optimized LSSVM with ICPSO

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

Reliability analysis on resonance for low-pressure compressor rotor blade based on least squares support vector machine with leave-one-out cross-validation

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