针对现有电力系统短期负荷预测精度低、数据处理量大、易受输入变量的影响等问题,提出了一种将离散Fréchet距离与LS-SVM相结合的短期负荷预测方法。分析总结了East-Slovakia Power Distribution Company提供的历年负荷数据,结合该...针对现有电力系统短期负荷预测精度低、数据处理量大、易受输入变量的影响等问题,提出了一种将离散Fréchet距离与LS-SVM相结合的短期负荷预测方法。分析总结了East-Slovakia Power Distribution Company提供的历年负荷数据,结合该地区的用电规律,通过引入离散Fréchet距离,建立离散曲线相似性的数学模型,选取出与基准曲线形状相似的相似日,利用相似日负荷数据对LS-SVM预测模型进行训练。经过仿真验证,并与标准LS-SVM模型得到的结果对比,所提预测方法明显提高了预测精度。展开更多
A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classif...A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.展开更多
文摘针对现有电力系统短期负荷预测精度低、数据处理量大、易受输入变量的影响等问题,提出了一种将离散Fréchet距离与LS-SVM相结合的短期负荷预测方法。分析总结了East-Slovakia Power Distribution Company提供的历年负荷数据,结合该地区的用电规律,通过引入离散Fréchet距离,建立离散曲线相似性的数学模型,选取出与基准曲线形状相似的相似日,利用相似日负荷数据对LS-SVM预测模型进行训练。经过仿真验证,并与标准LS-SVM模型得到的结果对比,所提预测方法明显提高了预测精度。
文摘A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.
