CAJ | 학술논문

提出了基于蚁群算法（ACO）优化的模糊神经网络垂直切换算法ACO-FNN，综合考虑了信号强度、移动速度、可用带宽等因素进行模糊神经网络处理，并采用蚁群算法进行优化，调整隶属度函数的参数。仿真结果表明，该算法能够在减少乒乓效应的基础上更好的保证用户的服务质量QOS。
제출료기우의군산법（ACO）우화적모호신경망락수직절환산법ACO-FNN，종합고필료신호강도、이동속도、가용대관등인소진행모호신경망락처리，병채용의군산법진행우화，조정대속도함수적삼수。방진결과표명，해산법능구재감소핑퐁효응적기출상경호적보증용호적복무질량QOS。
An ant colony optimization (ACO)-fuzzy neural network access selection algorithm optimized on the basis of AOC algorithm is put forward, which takes radio signal strength, terminal moving speed and network bandwidth into consideration to process fuzzy neural network processing module. The specialty of the algorithm is using ACO to optimize and adjust the parameters of membership function. The simulation results show that the proposed algorithm can decrease the packet dropping probability, reduce the happening times of Ping-Pong effect and decrease the blocking probability on the consideration of guaranteeing the QoS for users.