针对地铁车轮磨耗数据时间跨度较长引起的长期依赖问题,为了进一步提升预测精度,提出一种将麻雀搜索算法(sparrow search algorithm,SSA)优化双向长短期记忆网络(bidirectional long short term memory,Bi LSTM)的改进BiLSTM(SSA-BiLSTM...针对地铁车轮磨耗数据时间跨度较长引起的长期依赖问题,为了进一步提升预测精度,提出一种将麻雀搜索算法(sparrow search algorithm,SSA)优化双向长短期记忆网络(bidirectional long short term memory,Bi LSTM)的改进BiLSTM(SSA-BiLSTM)网络模型,用于地铁车轮磨耗预测。首先,利用麻雀搜索算法对双向长短期记忆网络算法的神经元个数、迭代次数、输入批量和学习率等超参数在给定范围内进行寻优,得到参数最优值;然后,以参数最优值来构建改进BiLSTM网络模型,对车轮磨耗进行预测分析;最后,以车轮踏面磨耗和轮缘磨耗作为研究对象,将某地铁1车厢1号车轮的现场实测历史磨耗数据作为输入,对该模型进行训练及验证分析,并与多层感知机(multilayer perceptron,MLP)、LSTM、BiLSTM以及SSA-LSTM模型的预测结果进行对比。研究结果表明:SSA-Bi-LSTM模型的车轮磨耗预测精度更高,与LSTM、BiLSTM以及SSA-LSTM网络模型相比,踏面磨耗的平均绝对百分误差(mean absolute percentage error,MAPE)分别降低了13.28%、10.32%、1.47%,轮缘磨耗分别降低了9.5%、0.46%、0.02%;分别对同一地铁2号、4号车厢的1号位置车轮磨耗进行预测,并与磨耗实测数据进行对比,踏面磨耗的平均绝对百分比误差分别为1.34%、1.42%,轮缘磨耗的平均绝对百分比误差分别为0.18%、0.19%,验证了本文所提模型具有良好的泛化性,为地铁轮对智能化管理提供理论支持,延长车轮使用寿命。展开更多
安全是民航业的核心主题。针对目前民航非计划事件分析严重依赖专家经验及分析效率低下的问题,文章提出一种结合Word2vec和双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络模型的民航非计划事件分析方法。首先采...安全是民航业的核心主题。针对目前民航非计划事件分析严重依赖专家经验及分析效率低下的问题,文章提出一种结合Word2vec和双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络模型的民航非计划事件分析方法。首先采用Word2vec模型针对事件文本语料进行词向量训练,缩小空间向量维度;然后通过BiLSTM模型自动提取特征,获取事件文本的完整序列信息和上下文特征向量;最后采用softmax函数对民航非计划事件进行分类。实验结果表明,所提出的方法分类效果更好,能达到更优的准确率和F 1值,对不平衡数据样本同样具有较稳定的分类性能,证明了该方法在民航非计划事件分析上的适用性和有效性。展开更多
科学有效地预测水质对于水环境的可持续发展和人类健康具有重要意义,为此以固原市某黄河断面的水质监测数据为研究对象,提出了基于指标客观性的权重赋权(Criteria Importance Though Intercriteria Correlation,CRITIC)法和改进的秃鹰搜...科学有效地预测水质对于水环境的可持续发展和人类健康具有重要意义,为此以固原市某黄河断面的水质监测数据为研究对象,提出了基于指标客观性的权重赋权(Criteria Importance Though Intercriteria Correlation,CRITIC)法和改进的秃鹰搜索(Improved Bald Eagle Search,IBES)算法优化双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)的组合水质等级预测模型。首先,采用CRITIC法确定各水质指标的权重,加权求和获得一项综合水质指标,从而提出一种改进的水质评价指标体系,以为BiLSTM提供更丰富、更可靠的水质特征信息。其次,在训练过程中引入Logistic映射和莱维飞行策略,并设计交叉共享及准反向搜索策略优化秃鹰搜索(Bald Eagle Search,BES)算法,以提升其种群多样性,增强寻优能力。最后,通过IBES算法迭代寻找BiLSTM的最佳学习率、隐藏层节点数以及正则化系数的超参数组合,进一步提高其预测水平。结果显示:与IBES-BiLSTM、BES-BiLSTM、GA-BiLSTM、PSO-BiLSTM和BiLSTM等模型相比,CRITIC-IBES-BiLSTM模型进行水质等级预测的准确率、精准率、召回率及F_(1)均最高,且具有更好的稳定性。展开更多
空调负荷的精准预测对建筑空调系统优化控制具有重要意义。为提高空调负荷预测精度,提出了一种基于奇异谱分析(SSA,Singular Spectrum Analysis)的卷积神经网络(CNN,Convolutional Neural Network)和双向长短时记忆网络(BiLSTM,Bidirect...空调负荷的精准预测对建筑空调系统优化控制具有重要意义。为提高空调负荷预测精度,提出了一种基于奇异谱分析(SSA,Singular Spectrum Analysis)的卷积神经网络(CNN,Convolutional Neural Network)和双向长短时记忆网络(BiLSTM,Bidirectional Long Short Term Memory)短期空调负荷预测模型。使用皮尔森相关系数选取与空调负荷高相关性特征。针对空调负荷的波动性和随机性,采用SSA将空调负荷分解为多个分量,同时将各个分量带入CNN-BiLSTM模型进行预测,该模型利用了CNN的特征提取和BiLSTM的双向学习能力,并将各个分量预测结果进行重构。通过不同建筑类型的空调数据对该模型进行验证分析,发现所提出模型在预测办公建筑空调负荷中RMSE、MAPE和MAE为19.47RT、14.72RT和2.33%,在预测商业建筑空调负荷中RMSE、MAPE和MAE为82.5RT、34.21RT和0.87%。结果表明,所提出的模型具有普适性且精度较高,可进行推广应用。展开更多
We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-tim...We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.展开更多
In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciat...In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.展开更多
针对风电功率序列具有波动性和较高复杂度的特点,提出了一种基于麻雀算法(sparrow search algorithm,SSA)优化的双向长短期记忆神经网络(bidirectional long and short-term memory neural network,BiLSTM)和奇异谱分析的短期风电功率...针对风电功率序列具有波动性和较高复杂度的特点,提出了一种基于麻雀算法(sparrow search algorithm,SSA)优化的双向长短期记忆神经网络(bidirectional long and short-term memory neural network,BiLSTM)和奇异谱分析的短期风电功率预测模型。首先,采用奇异值分析对历史功率数据进行特征提取,去噪处理减少噪声信息干扰;其次,利用麻雀算法对BiLSTM模型超参数寻优,以BiLSTM为基础构建风电功率预测模型,提高了模型训练效率;最后,采用某风电场的运行数据验证模型精度并对比其他模型验证模型合理性。实验结果表明:改进后的模型相对于基准模型,绝对误差降低了14.2%,均方根误差降低了4.24%,本文所提改进BiLSTM模型具有较好的预测性能,能有效提高短期风电功率预测的精度。展开更多
文摘针对地铁车轮磨耗数据时间跨度较长引起的长期依赖问题,为了进一步提升预测精度,提出一种将麻雀搜索算法(sparrow search algorithm,SSA)优化双向长短期记忆网络(bidirectional long short term memory,Bi LSTM)的改进BiLSTM(SSA-BiLSTM)网络模型,用于地铁车轮磨耗预测。首先,利用麻雀搜索算法对双向长短期记忆网络算法的神经元个数、迭代次数、输入批量和学习率等超参数在给定范围内进行寻优,得到参数最优值;然后,以参数最优值来构建改进BiLSTM网络模型,对车轮磨耗进行预测分析;最后,以车轮踏面磨耗和轮缘磨耗作为研究对象,将某地铁1车厢1号车轮的现场实测历史磨耗数据作为输入,对该模型进行训练及验证分析,并与多层感知机(multilayer perceptron,MLP)、LSTM、BiLSTM以及SSA-LSTM模型的预测结果进行对比。研究结果表明:SSA-Bi-LSTM模型的车轮磨耗预测精度更高,与LSTM、BiLSTM以及SSA-LSTM网络模型相比,踏面磨耗的平均绝对百分误差(mean absolute percentage error,MAPE)分别降低了13.28%、10.32%、1.47%,轮缘磨耗分别降低了9.5%、0.46%、0.02%;分别对同一地铁2号、4号车厢的1号位置车轮磨耗进行预测,并与磨耗实测数据进行对比,踏面磨耗的平均绝对百分比误差分别为1.34%、1.42%,轮缘磨耗的平均绝对百分比误差分别为0.18%、0.19%,验证了本文所提模型具有良好的泛化性,为地铁轮对智能化管理提供理论支持,延长车轮使用寿命。
文摘安全是民航业的核心主题。针对目前民航非计划事件分析严重依赖专家经验及分析效率低下的问题,文章提出一种结合Word2vec和双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络模型的民航非计划事件分析方法。首先采用Word2vec模型针对事件文本语料进行词向量训练,缩小空间向量维度;然后通过BiLSTM模型自动提取特征,获取事件文本的完整序列信息和上下文特征向量;最后采用softmax函数对民航非计划事件进行分类。实验结果表明,所提出的方法分类效果更好,能达到更优的准确率和F 1值,对不平衡数据样本同样具有较稳定的分类性能,证明了该方法在民航非计划事件分析上的适用性和有效性。
文摘科学有效地预测水质对于水环境的可持续发展和人类健康具有重要意义,为此以固原市某黄河断面的水质监测数据为研究对象,提出了基于指标客观性的权重赋权(Criteria Importance Though Intercriteria Correlation,CRITIC)法和改进的秃鹰搜索(Improved Bald Eagle Search,IBES)算法优化双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)的组合水质等级预测模型。首先,采用CRITIC法确定各水质指标的权重,加权求和获得一项综合水质指标,从而提出一种改进的水质评价指标体系,以为BiLSTM提供更丰富、更可靠的水质特征信息。其次,在训练过程中引入Logistic映射和莱维飞行策略,并设计交叉共享及准反向搜索策略优化秃鹰搜索(Bald Eagle Search,BES)算法,以提升其种群多样性,增强寻优能力。最后,通过IBES算法迭代寻找BiLSTM的最佳学习率、隐藏层节点数以及正则化系数的超参数组合,进一步提高其预测水平。结果显示:与IBES-BiLSTM、BES-BiLSTM、GA-BiLSTM、PSO-BiLSTM和BiLSTM等模型相比,CRITIC-IBES-BiLSTM模型进行水质等级预测的准确率、精准率、召回率及F_(1)均最高,且具有更好的稳定性。
文摘空调负荷的精准预测对建筑空调系统优化控制具有重要意义。为提高空调负荷预测精度,提出了一种基于奇异谱分析(SSA,Singular Spectrum Analysis)的卷积神经网络(CNN,Convolutional Neural Network)和双向长短时记忆网络(BiLSTM,Bidirectional Long Short Term Memory)短期空调负荷预测模型。使用皮尔森相关系数选取与空调负荷高相关性特征。针对空调负荷的波动性和随机性,采用SSA将空调负荷分解为多个分量,同时将各个分量带入CNN-BiLSTM模型进行预测,该模型利用了CNN的特征提取和BiLSTM的双向学习能力,并将各个分量预测结果进行重构。通过不同建筑类型的空调数据对该模型进行验证分析,发现所提出模型在预测办公建筑空调负荷中RMSE、MAPE和MAE为19.47RT、14.72RT和2.33%,在预测商业建筑空调负荷中RMSE、MAPE和MAE为82.5RT、34.21RT和0.87%。结果表明,所提出的模型具有普适性且精度较高,可进行推广应用。
基金This project was supported by the National Natural Science Foundation of China (60074008) .
文摘We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.
基金supported in part by the Gansu Province Higher Education Institutions Industrial Support Program:Security Situational Awareness with Artificial Intelligence and Blockchain Technology.Project Number(2020C-29).
文摘In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.
文摘针对风电功率序列具有波动性和较高复杂度的特点,提出了一种基于麻雀算法(sparrow search algorithm,SSA)优化的双向长短期记忆神经网络(bidirectional long and short-term memory neural network,BiLSTM)和奇异谱分析的短期风电功率预测模型。首先,采用奇异值分析对历史功率数据进行特征提取,去噪处理减少噪声信息干扰;其次,利用麻雀算法对BiLSTM模型超参数寻优,以BiLSTM为基础构建风电功率预测模型,提高了模型训练效率;最后,采用某风电场的运行数据验证模型精度并对比其他模型验证模型合理性。实验结果表明:改进后的模型相对于基准模型,绝对误差降低了14.2%,均方根误差降低了4.24%,本文所提改进BiLSTM模型具有较好的预测性能,能有效提高短期风电功率预测的精度。
