In order to make strategic decision on firms’ sharing reward program( SRP), a nested Stackelberg game is developed. The sharing behavior among users and the rewarding strategy of firms are modeled. The optimal sharin...In order to make strategic decision on firms’ sharing reward program( SRP), a nested Stackelberg game is developed. The sharing behavior among users and the rewarding strategy of firms are modeled. The optimal sharing bonus is worked out and the impact of social relationships among customers is discussed. The results show that the higher the bonus,the more efforts the inductor is willing to make to persuade the inductee into buying. In addition,the firms should take the social relationship into consideration when setting the optimal sharing bonus. If the social relationship is weak,there is no need to adopt the SRP. Otherwise,there are two ways to reward the inductors. Also,the stronger the social relationship,the fewer the sharing bonuses that should be offered to the inductors,and the higher the expected profits. As a result,it is reasonable for the firms to implement SRPs on the social media where users are familiar with each other.展开更多
针对当前雷达电子战越来越向着智能化的方向发展、传统干扰机无法适应环境变化、极大地降低了作战效果等问题,考虑将探测信号隐藏在干扰信号中,实现干扰探测共享信号,使侦察干扰机设备发射的干扰信号兼具探测的效果;针对当前干扰探测共...针对当前雷达电子战越来越向着智能化的方向发展、传统干扰机无法适应环境变化、极大地降低了作战效果等问题,考虑将探测信号隐藏在干扰信号中,实现干扰探测共享信号,使侦察干扰机设备发射的干扰信号兼具探测的效果;针对当前干扰探测共享信号中存在的复杂度低、频谱宽度较窄等问题,设计了一种基于多载频多相位编码(multi-carrier phase code,MCPC)的干扰探测共享信号,其具有良好的类噪声宽频谱特性以及较好的距离探测能力和速度探测能力,可以在对目标雷达实现压制干扰的同时对目标信号及周围环境进行隐蔽探测;为了使共享信号能够适应对战场环境的感知与博弈,进一步引入深度强化学习算法对MCPC干扰探测共享信号进行优化;首先在竞争深度Q学习网络(dueling deep Q-learning network,Du DQN)的基础上对Q值进行正则化,解决了Du DQN中易出现的由过估计导致的局部最优问题;其次,在奖励值中引入状态价值函数形成复合奖励值,将其称为复合奖励值竞争深度正则化Q学习网络(composite reward-dueling deep Q-learning network based on regularization,CR-Du DQNReg),使MCPC共享信号对奖励值的敏感度随自身状态调整,自适应优化相位编码初值,达到更好的干扰和隐蔽探测的效果.实验仿真结果表明:经CR-DuDQNReg算法优化后的MCPC共享信号频谱最高幅度提升17.48%,脉压最高幅度提升17.25%,多普勒模糊函数第1旁瓣幅度降低12.69%,且与传统深度强化学习算法相比,CR-Du DQNReg算法的优化效果更好.展开更多
基金The National Social Science Foundation of China(No.17BGL196)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYLX15_0193)
文摘In order to make strategic decision on firms’ sharing reward program( SRP), a nested Stackelberg game is developed. The sharing behavior among users and the rewarding strategy of firms are modeled. The optimal sharing bonus is worked out and the impact of social relationships among customers is discussed. The results show that the higher the bonus,the more efforts the inductor is willing to make to persuade the inductee into buying. In addition,the firms should take the social relationship into consideration when setting the optimal sharing bonus. If the social relationship is weak,there is no need to adopt the SRP. Otherwise,there are two ways to reward the inductors. Also,the stronger the social relationship,the fewer the sharing bonuses that should be offered to the inductors,and the higher the expected profits. As a result,it is reasonable for the firms to implement SRPs on the social media where users are familiar with each other.
文摘针对当前雷达电子战越来越向着智能化的方向发展、传统干扰机无法适应环境变化、极大地降低了作战效果等问题,考虑将探测信号隐藏在干扰信号中,实现干扰探测共享信号,使侦察干扰机设备发射的干扰信号兼具探测的效果;针对当前干扰探测共享信号中存在的复杂度低、频谱宽度较窄等问题,设计了一种基于多载频多相位编码(multi-carrier phase code,MCPC)的干扰探测共享信号,其具有良好的类噪声宽频谱特性以及较好的距离探测能力和速度探测能力,可以在对目标雷达实现压制干扰的同时对目标信号及周围环境进行隐蔽探测;为了使共享信号能够适应对战场环境的感知与博弈,进一步引入深度强化学习算法对MCPC干扰探测共享信号进行优化;首先在竞争深度Q学习网络(dueling deep Q-learning network,Du DQN)的基础上对Q值进行正则化,解决了Du DQN中易出现的由过估计导致的局部最优问题;其次,在奖励值中引入状态价值函数形成复合奖励值,将其称为复合奖励值竞争深度正则化Q学习网络(composite reward-dueling deep Q-learning network based on regularization,CR-Du DQNReg),使MCPC共享信号对奖励值的敏感度随自身状态调整,自适应优化相位编码初值,达到更好的干扰和隐蔽探测的效果.实验仿真结果表明:经CR-DuDQNReg算法优化后的MCPC共享信号频谱最高幅度提升17.48%,脉压最高幅度提升17.25%,多普勒模糊函数第1旁瓣幅度降低12.69%,且与传统深度强化学习算法相比,CR-Du DQNReg算法的优化效果更好.
