【目的】针对极限学习机(ELM)神经网络在室内可见光定位(VLP)中收敛不稳定,易陷入局部最优状态,导致定位精度降低的问题,文章引入了麻雀搜索算法(SSA)确定ELM神经网络的初始权值和阈值,提出了SSA-ELM神经网络算法。【方法】首先,采集定...【目的】针对极限学习机(ELM)神经网络在室内可见光定位(VLP)中收敛不稳定,易陷入局部最优状态,导致定位精度降低的问题,文章引入了麻雀搜索算法(SSA)确定ELM神经网络的初始权值和阈值,提出了SSA-ELM神经网络算法。【方法】首先,采集定位区域内接收信号强度(RSS)与位置信息作为指纹数据;然后,训练SSA-ELM神经网络并得到预测模型,将测试集数据输入预测模型得到待测位置的定位结果;最后,设计了仿真实验和测试平台。【结果】仿真表明,在立体空间模型中0、0.3、0.6和0.9 m 4个接收高度,平均误差分别为1.73、1.86、2.18和3.47 cm,与反向传播(BP)、SSA-BP和ELM定位算法相比,SSA-ELM神经网络算法定位精度分别提高了83.55%、45.71%和26.26%,定位时间分别降低了36.48%、17.69%和6.61%。实验测试表明,文章所提SSA-ELM神经网络算法的平均定位误差为3.75 cm,比未优化的ELM神经网络定位精度提高了16.38%。【结论】SSA对ELM神经网络具有明显的优化作用,能够显著降低定位误差,减少定位时间。展开更多
Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refer...Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.展开更多
文摘【目的】针对极限学习机(ELM)神经网络在室内可见光定位(VLP)中收敛不稳定,易陷入局部最优状态,导致定位精度降低的问题,文章引入了麻雀搜索算法(SSA)确定ELM神经网络的初始权值和阈值,提出了SSA-ELM神经网络算法。【方法】首先,采集定位区域内接收信号强度(RSS)与位置信息作为指纹数据;然后,训练SSA-ELM神经网络并得到预测模型,将测试集数据输入预测模型得到待测位置的定位结果;最后,设计了仿真实验和测试平台。【结果】仿真表明,在立体空间模型中0、0.3、0.6和0.9 m 4个接收高度,平均误差分别为1.73、1.86、2.18和3.47 cm,与反向传播(BP)、SSA-BP和ELM定位算法相比,SSA-ELM神经网络算法定位精度分别提高了83.55%、45.71%和26.26%,定位时间分别降低了36.48%、17.69%和6.61%。实验测试表明,文章所提SSA-ELM神经网络算法的平均定位误差为3.75 cm,比未优化的ELM神经网络定位精度提高了16.38%。【结论】SSA对ELM神经网络具有明显的优化作用,能够显著降低定位误差,减少定位时间。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72171136 and 72134004)Humanities and Social Science Research Project,Ministry of Education of China(Grant No.21YJC630157)+1 种基金the Natural Science Foundation of Shandong Province(Grant No.ZR2022MG008)Shandong Provincial Colleges and Universities Youth Innovation Technology of China(Grant No.2022RW066)。
文摘Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.
