Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry's law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cutoff conditions are met. Ten low-and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.
Although the individual threshing drum of a combine harvester was balanced on a dynamic balancing machine before it is assembled, there were still unbalances after multiple drums were assembled with the chain drive. In this paper, the double drums with a chain drive of a crawler combined harvester were selected as the research subject. The aim of this study was to develop a dynamic unbalance mode for grading chain drive double drums. Based on the dynamic unbalance characteristics of the main driven drum, the experimental research on the radial balance of the driven drum end face was carried out. It was known that the chain drive had a direct and obvious influence on the unbalanced phase of the drum. The unbalance of the drive load had an obvious effect on unbalanced amplitude of an active drum through the transfer characteristics of the chain drive. For the multi-stage transmission characteristics of a combine harvester, a step-by-step balanced grading chain drive double drum dynamic balancing method was practiced. Results showed that the unbalanced amplitude after balancing threshing drum I chain transmission mode of the combine harvester can be reduced by a maximum of 91%. Simultaneously, the unbalanced amplitude of threshing drum II can reduced by a maximum of 69.2%. The size and position of the wrap angle of the chain drive would directly affect the phase of the two equivalent unbalanced masses.
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