Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence w...Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.展开更多
为了预测深基坑支护桩水平变形的长期发展规律,在卷积神经网络(convolutional neural network,简称CNN)数据空间特征提取基础上,结合长短时记忆神经网络(long and short term memory,简称LSTM)分析数据的时序性和注意力机制(attention m...为了预测深基坑支护桩水平变形的长期发展规律,在卷积神经网络(convolutional neural network,简称CNN)数据空间特征提取基础上,结合长短时记忆神经网络(long and short term memory,简称LSTM)分析数据的时序性和注意力机制(attention mechanism,简称AM)的划分特征权重,构建了能够预测支护桩变形的AM-CNN-LSTM模型。以北京地区某深基坑工程为背景,基于灰色关联方法明确了影响支护桩最大变形的因素,通过构建的模型分析支护桩的单点变形规律,并与反向传播神经网络(back propagation neural network,简称BPNN)、CNN和传统CNN-LSTM模型的预测所得结果进行比较分析。研究结果表明:支护桩最大变形值与深基坑开挖深度、临空天数、支撑内力、土壤性质、桩的尺寸和嵌固深度等因素关联度较高;AM机制显著提升了初始数据信息挖掘深度和变形预测精度,通过梯度下降法不断更新直至满足误差要求;与BPNN、CNN及CNN-LSTM模型相比,AM-CNN-LSTM模型的应用对于支护桩的长期变形预测稳定性较好;通过与实测数据对比,AM-CNN-LSTM模型的预测精度误差在5%~10%以内。展开更多
基金Projects(10871031, 60474070) supported by the National Natural Science Foundation of ChinaProject(07A001) supported by the Scientific Research Fund of Hunan Provincial Education Department, China
文摘Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.
文摘为了预测深基坑支护桩水平变形的长期发展规律,在卷积神经网络(convolutional neural network,简称CNN)数据空间特征提取基础上,结合长短时记忆神经网络(long and short term memory,简称LSTM)分析数据的时序性和注意力机制(attention mechanism,简称AM)的划分特征权重,构建了能够预测支护桩变形的AM-CNN-LSTM模型。以北京地区某深基坑工程为背景,基于灰色关联方法明确了影响支护桩最大变形的因素,通过构建的模型分析支护桩的单点变形规律,并与反向传播神经网络(back propagation neural network,简称BPNN)、CNN和传统CNN-LSTM模型的预测所得结果进行比较分析。研究结果表明:支护桩最大变形值与深基坑开挖深度、临空天数、支撑内力、土壤性质、桩的尺寸和嵌固深度等因素关联度较高;AM机制显著提升了初始数据信息挖掘深度和变形预测精度,通过梯度下降法不断更新直至满足误差要求;与BPNN、CNN及CNN-LSTM模型相比,AM-CNN-LSTM模型的应用对于支护桩的长期变形预测稳定性较好;通过与实测数据对比,AM-CNN-LSTM模型的预测精度误差在5%~10%以内。
