A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations.The host-vector-predator nonlinear m...A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations.The host-vector-predator nonlinear model depends upon five groups or classes,host plant susceptible and infected populations,vectors population of susceptible and infected individuals and the predator population.An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms.For solving the hostvector-predator nonlinear model,a merit function is constructed using the differential model and its associated boundary conditions.The optimization of this merit function is performed using the computational strength of designed integrated heuristics based on interior point method and genetic algorithms.For the comparison,the obtained numerical solutions of networks models optimized with efficacy of global search of genetic algorithm and local search with interior point method have been compared with the Adams numerical solver based results or outcomes.Moreover,the statistical analysis will be performed to check the reliability,robustness,viability,correctness and competency of the designed integrated heuristics of unsupervised networks trained with genetic algorithm aid with interior point algorithm for solving the biological based host-vector-predator nonlinear model for sundry scenarios of paramount interest.展开更多
为提升DV-Hop(distance vector-hop)算法定位精度水平,提出一种基于割圆术的DV-Hop全局优化(circle-cutting technique for global optimization of DV-Hop algorithm,CTGO-DV-Hop)算法。该算法精确了最小跳数,引入权值模型对全部锚节...为提升DV-Hop(distance vector-hop)算法定位精度水平,提出一种基于割圆术的DV-Hop全局优化(circle-cutting technique for global optimization of DV-Hop algorithm,CTGO-DV-Hop)算法。该算法精确了最小跳数,引入权值模型对全部锚节点的平均单跳距离进行优化,并采用加权最小二乘递推算法对未知节点坐标进行拟合。实验测试多种因素对于算法的影响,结果表明,所提出的CTGO-DV-Hop算法表现出良好的性能,相较于DV-Hop、OCSLC-DV-Hop和WOCS-DV-Hop,平均定位误差的最大降幅为70%左右,适用于构建节点数目大、锚节点比例低的无线传感器网络系统。展开更多
电力电子设备在配电网的高渗透趋势导致谐波源密集化、分散化、广域化,传统点对点的谐波治理方式不再适用。该文从全网各节点电压畸变整体最优出发,提出一种基于电压检测型有源电力滤波器(voltage detection active power filters,VDAPF...电力电子设备在配电网的高渗透趋势导致谐波源密集化、分散化、广域化,传统点对点的谐波治理方式不再适用。该文从全网各节点电压畸变整体最优出发,提出一种基于电压检测型有源电力滤波器(voltage detection active power filters,VDAPF)的分布式全局优化治理方案。通过谐波治理灵敏度分析,构建以VDAPF接入点为参考的谐波治理分区算法;建立反映治理强度与谐波电压关系的VDAPF本地运行控制特性及其参数选取方法,实现谐波时变污染的在线控制;构建分布式VDAPF运行点优化配置模型,在长时间尺度内确定分布式治理系统的最优运行点。通过与电流检测型APF的对比分析,结果表明所提治理方案可以协调控制全网谐波电压,实现谐波动态治理,治理效果更明显。展开更多
基金This research received funding support from the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation(Grant Number B05F640088).
文摘A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations.The host-vector-predator nonlinear model depends upon five groups or classes,host plant susceptible and infected populations,vectors population of susceptible and infected individuals and the predator population.An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms.For solving the hostvector-predator nonlinear model,a merit function is constructed using the differential model and its associated boundary conditions.The optimization of this merit function is performed using the computational strength of designed integrated heuristics based on interior point method and genetic algorithms.For the comparison,the obtained numerical solutions of networks models optimized with efficacy of global search of genetic algorithm and local search with interior point method have been compared with the Adams numerical solver based results or outcomes.Moreover,the statistical analysis will be performed to check the reliability,robustness,viability,correctness and competency of the designed integrated heuristics of unsupervised networks trained with genetic algorithm aid with interior point algorithm for solving the biological based host-vector-predator nonlinear model for sundry scenarios of paramount interest.
文摘为提升DV-Hop(distance vector-hop)算法定位精度水平,提出一种基于割圆术的DV-Hop全局优化(circle-cutting technique for global optimization of DV-Hop algorithm,CTGO-DV-Hop)算法。该算法精确了最小跳数,引入权值模型对全部锚节点的平均单跳距离进行优化,并采用加权最小二乘递推算法对未知节点坐标进行拟合。实验测试多种因素对于算法的影响,结果表明,所提出的CTGO-DV-Hop算法表现出良好的性能,相较于DV-Hop、OCSLC-DV-Hop和WOCS-DV-Hop,平均定位误差的最大降幅为70%左右,适用于构建节点数目大、锚节点比例低的无线传感器网络系统。
文摘电力电子设备在配电网的高渗透趋势导致谐波源密集化、分散化、广域化,传统点对点的谐波治理方式不再适用。该文从全网各节点电压畸变整体最优出发,提出一种基于电压检测型有源电力滤波器(voltage detection active power filters,VDAPF)的分布式全局优化治理方案。通过谐波治理灵敏度分析,构建以VDAPF接入点为参考的谐波治理分区算法;建立反映治理强度与谐波电压关系的VDAPF本地运行控制特性及其参数选取方法,实现谐波时变污染的在线控制;构建分布式VDAPF运行点优化配置模型,在长时间尺度内确定分布式治理系统的最优运行点。通过与电流检测型APF的对比分析,结果表明所提治理方案可以协调控制全网谐波电压,实现谐波动态治理,治理效果更明显。
