مقالههای Lan YU
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
۱Improved Support Vector Machine Regression in Multi–Step–Ahead Prediction for Tunnel Surrounding Rock Displacement
اطلاعات انتشار: Scientia Iranica، بيست و يكم،شماره۴، ۲۰۱۴، سال ۰
تعداد صفحات: ۸
A dependable long–term prediction of tunnel surrounding rock displacement is an effective way to predict the rock displacement values into the future. A multi–step–ahead prediction model, which is based on support vector machine (SVM), is proposed for tunnel surrounding rock displacement prediction. To improve the performance of SVM, parameter identification is used for SVM. In addition, to treat with the time–varying features of tunnel surrounding rock displacement, a forgetting factor is introduced to adjust the weights between new and old data.At last, the data from the Chijiangchong tunnel are selected to examine the performance of the prediction model. Comparative results were presented between SVMFF (SVM with a forgetting factor) and artificial neural network with a forgetting factor (ANNFF) show that SVMFF is generally better than ANNFF. This indicates that a forgetting factor can effectively improve the performance of SVM, especially for the time–varying problems.
۲Merged Automobile Maintenance Part Delivery Problem Using an Improved Artificial Bee Colony Algorithm
اطلاعات انتشار: Scientia Iranica، بيست و دوم،شماره۳، ۲۰۱۵، سال ۰
تعداد صفحات: ۱۳
The merged automobile maintenance part delivery problem will attract interests from the merged company due to the reduced delivery cost by collaborative delivery among several automobile part depots. Since the delivery problem is a very complex problem, Voronoi diagram is adopted to simplify this delivery problem by splitting customers into several sets. Then, this paper attempts to solve this delivery problem by using of artificial bee colony algorithm. To improve the performance of the artificial bee colony algorithm, an adaptive strategy is used to control the proportion of scouts and leaders. At last, the computational results for 23 benchmark problems indicate that the proposed algorithm is an effective method to solve the multi–depot vehicle routing problem. Furthermore, the results of a merged automobile maintenance part delivery problem also indicate that the improved artificial bee colony algorithm with Voronoi diagram is feasible for solving this kind of delivery problem.
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