摘要
提出了一种具有较强抗突发干扰能力的非单点模糊径向基函数(Radial Basis Function,RBF)网络判决反馈均衡器.该方法将具有前置滤波特性的非单点模糊化技术引入RBF网络,利用梯度下降法自适应调整参数.通过仿真实验,并与基于径向基函数网络的判决反馈均衡器(Radial Basis Function Network-Decision Feedback Equalizer,RBFN-DFE)和传统判决反馈均衡器(Decision Feedback Equalizer,DFE)进行比较,结果证明该方法抗突发干扰能力强,误码性能好.
A non-singleton fuzzy radial basis function（RBF） network based decision feedback equalizer is proposed in this paper for severely nonlinear distorted channels with burst jamming. The method intro- dueed non-singleton fuzzy technology with preceding filtering capability into RBF network, and adjusted the tunable parameters by gradient-descent algorithm. Simulation is carried out to compare it with other nonlinear channel equalizers. The result shows the method has better performance on anti-burst jamming and bit error rate.
出处
《电波科学学报》
CSCD
北大核心
2017年第1期84-89,共6页
Chinese Journal of Radio Science
基金
中国博士后科学基金特别资助(2016T91018)
国家自然科学基金资助课题(60772056)
关键词
均衡器
判决反馈
突发干扰
非单点模糊系统
神经网络
equalizer
decision feedback
burst jamming
non-singleton fuzzy system
neural network
