CAJ | 학술논문

文章提出了微粒群优化的3层BP神经网络在线3步预测智能光网络带宽按需分配(BoD)业务流量模型,并改进了微粒群优化算法.对适应值较差的一部分微粒施加随机扰动,然后评估扰动效果,接受进化同时以一定概率接受退化.按3步预测中的最大值分配带宽,降低了突发业务阻塞率.仿真结果表明,该预测模型适应突发性、多样性BoD业务流量的在线预测.
문장제출료미립군우화적3층BP신경망락재선3보예측지능광망락대관안수분배(BoD)업무류량모형,병개진료미립군우화산법.대괄응치교차적일부분미립시가수궤우동,연후평고우동효과,접수진화동시이일정개솔접수퇴화.안3보예측중적최대치분배대관,강저료돌발업무조새솔.방진결과표명,해예측모형괄응돌발성、다양성BoD업무류량적재선예측.
In this paper,a 3-layer PSO-trained BP Artificial Neural Networks (ANN) model is proposed for the on-line prediction of Bandwidth on Demand (BoD) ASON services and the Particle Swarm Optimization (PSO) algorithm improved. Particles with poor fitness are imposed with disturbances at random and then the disturbance effect is evaluated. The improved particles are accepted and at the same time the degenerated ones are also accepted in a certain probability. The maximum of the three predicted data is considered as the future bandwidth distribution,thus reducing the blocking rate of the bursting service. The simulation results show that this prediction model is appropriate for the on-line prediction of bursting and diversified BoD services.