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Camber Prediction Based on Fusion Method with Mechanism Model and Machine Learning in Plate Rolling
2021
ISIJ International
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Prediction model of camber synthesis leveling based on PSO-LSSVM algorithm is used, the relative error is within ± 5% of both the training set and the testing set. ...
The research result reveals that this method is suitable for camber prediction and model optimization in plate rolling process. ...
Foundation of China (51634002), for financial support. ...
doi:10.2355/isijinternational.isijint-2020-357
fatcat:kgudd6kmsffglfajyek5b53sea
Rolling Force Prediction of Hot Rolling Based on GA-MELM
2019
Complexity
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In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely ...
, PSO-GA-MELM algorithm, which takes MELM as the basic model for rolling force prediction. ...
Prediction results of rolling force prediction model based on PSO-ELM are shown in Figure 4 . Prediction results of rolling force prediction model based on PSO-GA-MELM are shown in Figure 5 . ...
doi:10.1155/2019/3476521
fatcat:g5j7zgowojgz5jmr2x25fubsqm
Hybrid PSO-SVM Based Method for Degradation Process Prediction of Reciprocating Seal
2019
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
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In this paper, we have built and trained a hybrid PSO-SVM model to predict the reciprocating seal degradation. ...
Particle swarm optimization is used to optimize the penalty factor and kernel parameter of SVM model. ...
Support Vector Regression Support Vector Machine based on statistical learning theory is put forward by Vapnik in 1995. ...
doi:10.36001/phmconf.2019.v11i1.852
fatcat:acfdlhcpere3zg3g4jowikngyq
Research and Application of a Rolling Gap Prediction Model in Continuous Casting
2019
Metals
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Second, a support vector machine (SVM) model using particle swarm optimization (PSO) was proposed to optimize the parameters and perform roll gap prediction. ...
In order to improve the precision and timeliness of the roll gap value control, we proposed a rolling gap value prediction (RGVP) method based on the continuous casting process parameters. ...
Similarly, PSO was used to select the optimal parameters of the support vector machine (SVM) kernel function in this paper. ...
doi:10.3390/met9030380
fatcat:4iltm4cq3vbe7fdz4ourdgdk4a
PSO-LSSVR Assisted GPS/INS Positioning in Occlusion Region
2019
Sensors
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This paper therefore proposes a scheme of occlusion region navigation based on least squares support vector regression (LSSVR), and particle swarm optimization (PSO), used to seek the global optimal parameters ...
to optimize the regression parameters of LSSVR. ...
Least Squares Support Vector Regression Algorithm Support vector machine (SVM) is based on statistical learning theory and has the advantage of strong model generalization ability [24] . ...
doi:10.3390/s19235256
pmid:31795374
fatcat:lu5cr3xf6jbstg7u47ix4vsz7e
Study on Rail Profile Optimization Based on the Nonlinear Relationship between Profile and Wear Rate
2017
Mathematical Problems in Engineering
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as constraint conditions, the support vector machine regression theory was used to fit the nonlinear relationship between rail profile and its wear rate. ...
The result showed that the average relative error of support vector machine regression model remained less than 10% after a number of training processes. ...
Acknowledgments This work was supported by National Natural Science Foundation of China (no. 51208318 and no. 51208319), Hebei Provincial Natural Science Foundation of China (no. E2015210099 and no. ...
doi:10.1155/2017/6956514
fatcat:43zt5cdwwnal3cnocitf6jhk6i
A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation
2020
Sensors
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Least-square support vector machine (LSSVM) is used to predict product concentration in real time. ...
The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear ...
Soft-sensors for lipid
fermentation variables based on PSO Support Vector Machine (PSO-SVM). ...
doi:10.3390/s20113335
pmid:32545372
fatcat:xihkkcudv5bpfio5fr2tdaszfm
Optimization of the irregular shape rolling process with an artificial neural network
2001
Journal of Materials Processing Technology
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Developing artificial neural network (ANN), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. ...
Moreover, an optimal state of process for specific range of input parameters is obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. ...
Acknowledgments The authors are grateful for the research support of the Iran National Science Foundation (INSF). ...
doi:10.1016/s0924-0136(01)00692-6
fatcat:k5pyqacusnaklbo37ntbin6z2e
Performance optimization of banana vibrating screens based on PSO-SVR under DEM simulations
2019
Journal of Vibroengineering
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Based on the simulation data this paper applied the least squares support vector machines (LS-SVM) to establish relationships between vibrating parameters of banana screen and screening performance. ...
LS-SVM based on statistical theory can effectively solve the mapping problem of small sample. ...
Acknowledgements The authors gratefully acknowledged the support from the Program for scientific and technological innovation flats of Fujian Province (2014H2002). ...
doi:10.21595/jve.2018.19543
fatcat:ct4cko2t6fb5vgqauvtqadpsfm
Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model
2020
International Journal of Interactive Multimedia and Artificial Intelligence
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This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. ...
The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). ...
Acknowledgment Authors wish to acknowledge the computational support provided by the Department of Mathematics at University of Oviedo. ...
doi:10.9781/ijimai.2020.11.004
fatcat:t6etxt2qfrginawujbvdhxrbwy
Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel
2021
Materials
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The objective of this work is to develop a surface roughness prediction method based on the processing of vibration signals during steel end milling operation performed on a vertical CNC machining center ...
The analysis resulted in the extraction of 245 features that were used in the evolutionary optimization study to determine optimal cutting conditions based on the measured surface roughness of the milled ...
Particle Swarm Optimization (PSO) PSO is a population-based stochastic optimization technique inspired by the swarm flocking behavior in nature [24] . ...
doi:10.3390/ma14195494
pmid:34639893
pmcid:PMC8509771
fatcat:m7t5qo3j7zfctpp2pqaljjqbie
An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints
2019
Entropy
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neighborhood rough set reduction algorithm, the particle swarm optimization (PSO) algorithm and support vector regression machine (SVRM) are combined to construct a fatigue life prediction model of titanium ...
The PSO-SVRM model for fatigue life prediction of titanium alloy welded joints is established in the suggested fatigue characteristic domains. ...
At present, PSO algorithm has been used to optimize the parameters of least squares support vector machines in order to construct an optimal LS-SVM classifies [32] and an improved adaptive particle swarm ...
doi:10.3390/e21020117
pmid:33266833
fatcat:e3nmvclxzjfa7gkm6ssn3fcb4i
Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression
2022
Journal of Marine Science and Engineering
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The support vector regression (SVR) model optimised through particle swarm optimisation (PSO) was used to complete the black-box motion modelling and vehicle prediction. ...
The high-precision model parameter combination was obtained using PSO, and, subsequently, the black-box modelling and prediction of the vehicle were realised. ...
hyperparameters of support vector regression (SVR) were not optimally discussed. ...
doi:10.3390/jmse10050575
fatcat:wjpbiyv5ofan7ptqe4g4ygnxr4
Optimal design of hydraulic support landing platform for a four-rotor dish-shaped UUV using particle swarm optimization
2016
International Journal of Naval Architecture and Ocean Engineering
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Then, the response surface model of the optimization objective is established. The intelligent particle swarm optimization (PSO) is applied to finding the optimal solution. ...
The main goal of this paper is to develop a quick method to optimize the design of hydraulic support landing platform for the new UUV. ...
Acknowledgment The authors would like to acknowledge the support of the Grant No.51179159 from the National Natural Science Foundation of China. ...
doi:10.1016/j.ijnaoe.2016.05.007
fatcat:cs6sf2dzljdvrezoxyvwgevjge
A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process
2020
Metals
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In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute ...
A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. ...
Support Vector Machine (SVM) Support Vector Machine (SVM) is a supervised learning method developed from statistical learning theory to analyze data and pattern recognition, which can be used to classify ...
doi:10.3390/met10050685
fatcat:ztjabcteczayvavphwknzoia2y
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