摘要
未来信息化的网络中心战中,网络拥塞问题成为制约战争信息有效传输的瓶颈。单神经元PID拥塞控制算法中,系统的稳定性和超调量等性能对于神经元的增益系数K的依赖性很大,结合无需辨识的自适应控制算法,动态地调整增益系数K;另外,为了进一步提高控制器品质,加入调整因子在线调整神经元权值的学习率;最后将改进后的算法应用到战术网络拥塞控制中。仿真结果验证了所提算法的有效性。
In the information network centrie warfare, the network congestion problem becomes the bottleneck of warfare information transformation. In single neuron PID congestion control, the neuron gain K is the main factor affecting the system performance of stability and overshoot, which is totally depended on expertise and is highly subjective. In this paper, the neuron gain K is adjusted dynamically combined with identification-free adaptive control algorithm. In addition, in order to improve the quality of the controller, the adjustment factor is introduced to adjust the learning rate of neuron weight. The simulation results in the tactical network verified the effectiveness and feasibility of the improved algorithm.
出处
《电光与控制》
北大核心
2013年第10期14-17,共4页
Electronics Optics & Control
基金
国家自然科学基金(11102080)
中央高校基本科研业务费专项资金(NP2011049
NZ2012005)
航空科学基金资助项目(20095152028)
关键词
战术网络
拥塞控制
单神经元
自适应控制
tactical network
congestion control
single neuron
adaptive control