摘要
针对相控阵雷达任务调度中任务优先级较难建立数学模型,从而影响任务调度效率的问题,提出一种基于自适应模糊神经网络的相控阵雷达任务调度算法。该算法:模糊控制部分能够利用模糊隶属度对多个目标参数值进行量化处理;神经网络部分可以智能地实现目标参数和任务优先级之间非线性映射。仿真结果表明,该方法有效,在目标数目饱和情况下,保证高优先级任务被调度的同时,使更多的任务得到调度执行,其性能优于传统任务调度方法。
As the task scheduling of phased array radar is a complex nonlinear optimization process,a mathematical model is difficult to be established for task priority,which may affect the efficiency of task scheduling. A phased array radar task scheduling algorithm is proposed based on self-adaptive fuzzy neural network. The proposed scheduling algorithm has neural network autonomous learning ability and fuzzy control capacity for dealing with uncertain information. The simulated result shows that the method is effective. The method can be used to schedule and implement more tasks while scheduling the tasks with higher priority under the condition of saturated object number.
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2016年第11期2010-2014,共5页
Acta Armamentarii
基金
国家自然科学基金项目(61302193)
全军军事类研究生资助项目(2014JY548)
关键词
兵器科学与技术
神经网络
模糊理论
相控阵雷达
优先级
任务调度
ordnance science and technology
neural network
fuzzy theory
phased array radar
priority
task scheduling
