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| Xie,Shengshi; Wang,Chunguang; Deng,Weigang. |
| ABSTRACT For the purpose of achieving the distribution of the potato-soil mixture and the appropriate parameters of the swing separating sieve, we conducted experiments using the 4SW-170 potato digger. The experiments consisted of two parts. In each part, the experimental factors were crank rotational speed, sieve inclination and machine forward speed. The difference is that the first part involved a single factor test, which selected the coverage of the potato-soil mixture as the evaluation indicator. In contrast, the second part involved an orthogonal test, which selected the obvious rate and damage rate as evaluation indexes. In the first part, it was observed that the coverage of the potato-soil mixture on the separating sieve reduced gradually with... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Swing separating sieve; Coverage of potato-soil mixture; Parameter optimization; Experimental studies. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000400548 |
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| Zeng, J; Tan, Zh; Matsunaga, T; Shirai, T. |
| A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a... |
| Tipo: Text |
Palavras-chave: Support vector machine for regression; SVM; LS-SVM; Machine learning; Parameter optimization; Global ocean CO2. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00676/78774/80949.pdf |
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