基于混合遗传神经网络的电动汽车充电站最优选址的研究

为了选择电动汽车的最优充电站址,提出了一种基于混合遗传神经网络的评价方法。首先利用Delphi法建立起充电站候选站址综合评价指标体系,然后用专家评价法对其打分,并且将获得的数据进行标准化,再应用自适应遗传算法优化神经网络的连接权值,利用三层神经网络对该指标进行评价,最后利用暂态误差方法,确定最优结果。用某实际算例证明了该方法的良好实用性。

Discussion on Data Synchronization Solution for Standby Visual Dispatching System

In order to select the optimal location of electric vehicle, a kind of evaluation method based on hybrid genetic artifi- cial neural network is proposed. First of all, Delphi method is used to establish the comprehensive evaluation index system of candidate charging stations, then the index system is marked by expert evaluation method and the obtained data are standard- ized. And then, the self - adaptive genetic algorithm is applied to optimize the [ink weight of neural network, and three - layer neural network is utilized to evaluate the index. At last, the transient error process is used to confirm the optimal result. An actual example has proved the favorable practicability of this proposed method.