Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed...Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.展开更多
为监测大坝运行过程中的异常状态,防范化解大坝溃坝等重大风险,基于大坝变形大样本、非线性监测数据,引入长短期记忆(Long Short Term Memory, LSTM)神经网络模型对大坝变形趋势进行预测,以测试样本的均方根误差最小为适应度函数,采用...为监测大坝运行过程中的异常状态,防范化解大坝溃坝等重大风险,基于大坝变形大样本、非线性监测数据,引入长短期记忆(Long Short Term Memory, LSTM)神经网络模型对大坝变形趋势进行预测,以测试样本的均方根误差最小为适应度函数,采用遗传算法(Genetic Algorithm, GA)优化LSTM模型参数,建立大坝变形GA-LSTM组合预测模型。以福建水口水电站大坝为例进行验证分析,并与LSTM模型和门控循环神经网络(Gated Recurrent Unit, GRU)模型预测结果进行对比分析。分析结果表明,GA-LSTM模型的预测效果和性能更佳,且相较于LSTM模型和GRU模型各测点预测误差均有减小,平均绝对误差减小量最高达6.92%。展开更多
针对电厂气体污染监测的需要,该文提出了一种融合多源无线传感器网络(Wireless Sensor Networks,WSN)与改进长短期记忆网络(Long Short Term Memory,LSTM)的气体污染监测与预警模型。通过设计多源WSN污染气体采集框架,实现了对电厂多种...针对电厂气体污染监测的需要,该文提出了一种融合多源无线传感器网络(Wireless Sensor Networks,WSN)与改进长短期记忆网络(Long Short Term Memory,LSTM)的气体污染监测与预警模型。通过设计多源WSN污染气体采集框架,实现了对电厂多种污染气体的高效采集,为预测模型提供高质量的数据输入。所设计的改进LSTM模型融合了模拟退火算法(Simulated Annealing,SA)和SVM模块,其中SA用于优化LSTM的超参数,SVM则作为分类器有效避免过拟合问题,使改进后的LSTM能够充分提取多源传感器数据的特征并进行准确预测。实验结果表明,结合SA和SVM模块有效提升了LSTM的预测性能,与其他分类算法相比表现出了明显的优势,准确率高达97.83%,相比于对比算法中表现最佳的BiLSTM提高了9.64%。展开更多
基金supported by the Hunan University of Science and Technology Doctoral Research Foundation Project(E51873).
文摘Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.
文摘为监测大坝运行过程中的异常状态,防范化解大坝溃坝等重大风险,基于大坝变形大样本、非线性监测数据,引入长短期记忆(Long Short Term Memory, LSTM)神经网络模型对大坝变形趋势进行预测,以测试样本的均方根误差最小为适应度函数,采用遗传算法(Genetic Algorithm, GA)优化LSTM模型参数,建立大坝变形GA-LSTM组合预测模型。以福建水口水电站大坝为例进行验证分析,并与LSTM模型和门控循环神经网络(Gated Recurrent Unit, GRU)模型预测结果进行对比分析。分析结果表明,GA-LSTM模型的预测效果和性能更佳,且相较于LSTM模型和GRU模型各测点预测误差均有减小,平均绝对误差减小量最高达6.92%。
文摘针对电厂气体污染监测的需要,该文提出了一种融合多源无线传感器网络(Wireless Sensor Networks,WSN)与改进长短期记忆网络(Long Short Term Memory,LSTM)的气体污染监测与预警模型。通过设计多源WSN污染气体采集框架,实现了对电厂多种污染气体的高效采集,为预测模型提供高质量的数据输入。所设计的改进LSTM模型融合了模拟退火算法(Simulated Annealing,SA)和SVM模块,其中SA用于优化LSTM的超参数,SVM则作为分类器有效避免过拟合问题,使改进后的LSTM能够充分提取多源传感器数据的特征并进行准确预测。实验结果表明,结合SA和SVM模块有效提升了LSTM的预测性能,与其他分类算法相比表现出了明显的优势,准确率高达97.83%,相比于对比算法中表现最佳的BiLSTM提高了9.64%。
