No abstract

t is important for dynamic characteristics of electromagnetic release with oil dashpot to calculate the oil damping force. The formula of the oil damping force is deduced by the theoretical formula of viscous damper or according to the experimental results. Therefore, a way to analyze oil damping force of electromagnetic release with oil dashpot is proposed based on Fluent due to the limitations of the two methods in this paper. The curve which is the speed of the plunger variations of the oil damping force is drawn through the numerical analysis of the flow field and the index of the speed is fitted.

The Forward Least-Squares Approximation (FLSA) SVM is a newly-emerged Least-Square SVM (LS-SVM) whose solution is extremely sparse. The algorithm uses the number of support vectors as the regularization parameter and ensures the linear independency of the support vectors which span the solution. This paper proposed a variant of the FLSA-SVM, namely, Reduced FLSA-SVM which is of reduced computational complexity and memory requirements. The strategy of “contexts inheritance” is introduced to improve the efficiency of tuning the regularization parameter for both the FLSA-SVM and the RFLSA-SVM algorithms. Experimental results on benchmark datasets showed that, compared to the SVM and a number of its variants, the RFLSA-SVM solutions contain a reduced number of support vectors, while maintaining competitive generalization abilities. With respect to the time cost for tuning of the regularize parameter, the RFLSA-SVM algorithm was empirically demonstrated fastest compared to FLSA-SVM, the LS-SVM, and the SVM algorithms.