为降低电网故障诊断中因人为主观因素的影响而造成的误差,提出了一种基于BP与分层变迁的加权模糊Petri网(weighted fuzzy Petri net,WFPN)相融合的输电线路故障诊断方法。根据故障信息确定出可疑元件,然后针对各元件分别建立它们的子模...为降低电网故障诊断中因人为主观因素的影响而造成的误差,提出了一种基于BP与分层变迁的加权模糊Petri网(weighted fuzzy Petri net,WFPN)相融合的输电线路故障诊断方法。根据故障信息确定出可疑元件,然后针对各元件分别建立它们的子模型和综合诊断模型。考虑到Petri网模型与BP神经网络在结构和形式上有一定的相似性,因此本文采用BP算法对Petri网模型中的权值进行训练。仿真结果表明该方法具有合理性及有效性。展开更多
Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimu...Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.展开更多
文摘为降低电网故障诊断中因人为主观因素的影响而造成的误差,提出了一种基于BP与分层变迁的加权模糊Petri网(weighted fuzzy Petri net,WFPN)相融合的输电线路故障诊断方法。根据故障信息确定出可疑元件,然后针对各元件分别建立它们的子模型和综合诊断模型。考虑到Petri网模型与BP神经网络在结构和形式上有一定的相似性,因此本文采用BP算法对Petri网模型中的权值进行训练。仿真结果表明该方法具有合理性及有效性。
文摘Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.
