评估指标权重的确定是影响智能汽车网络安全性评估的重要因素之一。针对传统确权方法忽略指标属性状态变化对评估指标权重影响的问题,提出了一种基于动态权重分配的网络安全评估模型。该模型首先对车辆自组织网络(vehicularAd Hoc netwo...评估指标权重的确定是影响智能汽车网络安全性评估的重要因素之一。针对传统确权方法忽略指标属性状态变化对评估指标权重影响的问题,提出了一种基于动态权重分配的网络安全评估模型。该模型首先对车辆自组织网络(vehicularAd Hoc network,VANET)进行安全目标分解与分析,构建其安全性评估指标体系。针对构建出的安全性评估指标体系,利用基于排序的确权算法对安全指标进行指标关联性分析,随后采用所提出的动态权重分配算法,计算指标体系中各个指标的动态权重,进而实现智能汽车VANET的安全性评估,得到安全等级评估结果。实验结果表明,该模型可以提升智能汽车VANET评估的合理性。展开更多
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method...Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.展开更多
文摘评估指标权重的确定是影响智能汽车网络安全性评估的重要因素之一。针对传统确权方法忽略指标属性状态变化对评估指标权重影响的问题,提出了一种基于动态权重分配的网络安全评估模型。该模型首先对车辆自组织网络(vehicularAd Hoc network,VANET)进行安全目标分解与分析,构建其安全性评估指标体系。针对构建出的安全性评估指标体系,利用基于排序的确权算法对安全指标进行指标关联性分析,随后采用所提出的动态权重分配算法,计算指标体系中各个指标的动态权重,进而实现智能汽车VANET的安全性评估,得到安全等级评估结果。实验结果表明,该模型可以提升智能汽车VANET评估的合理性。
文摘Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.
