首页> 外文会议>Proceedings of 2011 International Conference on Machine Learning and Cybernetics >Busy stations recognition of Hangzhou public free-bicycle system based on sixth order polynomial smoothing support vector machine
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Busy stations recognition of Hangzhou public free-bicycle system based on sixth order polynomial smoothing support vector machine

机译:基于六阶多项式平滑支持向量机的杭州公交免费系统忙站识别

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In China, Hangzhou is the first city to set up the Public Free-Bicycle System. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, how to list the busy station is very important to design the location of storehouses. Now, there are near 4000 stations in Hangzhou. In this paper, a new data classification method is used to recognize the busy station, which is called Support vector machine (SVM). The original model is a quadratical programming with linear inequalities constraints. In order to get the optimal solution, a new solution method is proposed. The constraints are moved away from the original optimization model by using the approximation solution in the feasible space. Three points under one control parameter smoothing function is used to smoothen the objective function of unconstrained model. It is a sixth order polynomial function. The smoothing performance is investigated. Actually, the busy stations can be recognized from the given data set.
机译:在中国,杭州是第一个建立公共免费自行车系统的城市。智能调度决策中存在很多技术问题。在这些问题中,如何列出繁忙的车站对于设计仓库的位置非常重要。现在,杭州有近4000个车站。本文采用一种新的数据分类方法来识别忙碌站点,称为支持向量机(SVM)。原始模型是具有线性不等式约束的二次规划。为了获得最优解,提出了一种新的求解方法。通过在可行空间中使用逼近解,可以将约束从原始优化模型中移开。一个控制参数平滑函数下的三点用于平滑无约束模型的目标函数。它是一个六阶多项式函数。研究了平滑性能。实际上,可以从给定的数据集中识别繁忙的车站。

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