The application of Bayesian network(BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these probl...The application of Bayesian network(BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed.First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.展开更多
重要输电通道风险评估和预测对状态检修和线路运维工作具有指导性意义,而采用传统长短时记忆(long and short-term memory,LSTM)网络对线路风险进行预测时,人为调参困难、预测精度较低,因此,提出了一种基于水波优化-因子分析-长短时记忆...重要输电通道风险评估和预测对状态检修和线路运维工作具有指导性意义,而采用传统长短时记忆(long and short-term memory,LSTM)网络对线路风险进行预测时,人为调参困难、预测精度较低,因此,提出了一种基于水波优化-因子分析-长短时记忆(water wave optimization-factor analysis-long and short-term memory,WWO-FALSTM)的重要输电通道风险准确评估与快速预测方法。首先,引入Levy分布、高斯–柯西变异算子和线性递减波高对WWO进行改进;其次,获取评估区多维致灾因子,并进行FA降维后作为网络输入,考虑孕灾环境敏感性和承灾体易损性计算出风险指数Rc作为网络输出;通过改进的WWO对LSTM进行不断优化,得到最优化LSTM模型;最后,采用最优化LSTM模型对重要输电通道进行风险预测。结果表明,该模型风险评估准确,模型预测较传统方法降低了误差,适用于输电通道风险评估与预测。展开更多
为提高工业控制系统信息安全风险评估的准确性,提出基于核主成分分析(kernel principal component analy,KPCA)和灰狼算法(grey wolf optimization algorithm,GWO)优化反向传播(back propagation,BP)神经网络的工业控制系统信息安全风...为提高工业控制系统信息安全风险评估的准确性,提出基于核主成分分析(kernel principal component analy,KPCA)和灰狼算法(grey wolf optimization algorithm,GWO)优化反向传播(back propagation,BP)神经网络的工业控制系统信息安全风险评估模型。即先使用KPCA法进行相关的风险因素分析,消除冗余变量,从而对神经网络的整个结构进行简化;为避免模型陷入局部解,在对BP神经网络的参数进行寻优后完成工业控制系统风险评估模型的建立,并在MATLAB环境下编写算法进行仿真,以此来验证评估模型的有效性。仿真结果表明,基于KPCA-GWO-BP的风险评估模型可以显著提高风险评估结果的准确率。展开更多
基金supported by the National Natural Science Foundation of China(Nos.41375002,51609254)the Provincial Natural Science Fund(BK20161464)of Jiangsu
文摘The application of Bayesian network(BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed.First, multivariate nonlinear planning is applied to the feedback error learning of parameters. A genetic algorithm is used to learn the probability distribution of nodes that lack quantitative data. Then, based on an improved grey relational analysis that considers the correlation of variation rate, the optimal weight that characterizes the correlation is calculated and the weighted BN is improved for decorrelation. An improved risk assessment model based on the weighted BN then is built. An assessment of sea ice disaster shows that the model can be applied for risk assessment with incomplete data and variable correlation.
文摘重要输电通道风险评估和预测对状态检修和线路运维工作具有指导性意义,而采用传统长短时记忆(long and short-term memory,LSTM)网络对线路风险进行预测时,人为调参困难、预测精度较低,因此,提出了一种基于水波优化-因子分析-长短时记忆(water wave optimization-factor analysis-long and short-term memory,WWO-FALSTM)的重要输电通道风险准确评估与快速预测方法。首先,引入Levy分布、高斯–柯西变异算子和线性递减波高对WWO进行改进;其次,获取评估区多维致灾因子,并进行FA降维后作为网络输入,考虑孕灾环境敏感性和承灾体易损性计算出风险指数Rc作为网络输出;通过改进的WWO对LSTM进行不断优化,得到最优化LSTM模型;最后,采用最优化LSTM模型对重要输电通道进行风险预测。结果表明,该模型风险评估准确,模型预测较传统方法降低了误差,适用于输电通道风险评估与预测。
文摘为提高工业控制系统信息安全风险评估的准确性,提出基于核主成分分析(kernel principal component analy,KPCA)和灰狼算法(grey wolf optimization algorithm,GWO)优化反向传播(back propagation,BP)神经网络的工业控制系统信息安全风险评估模型。即先使用KPCA法进行相关的风险因素分析,消除冗余变量,从而对神经网络的整个结构进行简化;为避免模型陷入局部解,在对BP神经网络的参数进行寻优后完成工业控制系统风险评估模型的建立,并在MATLAB环境下编写算法进行仿真,以此来验证评估模型的有效性。仿真结果表明,基于KPCA-GWO-BP的风险评估模型可以显著提高风险评估结果的准确率。
