基于动态递归模糊神经网络盲均衡算法的研究  

Blind Equalization Algorithm Based on Dynamic Recurrent Fuzzy Neural Network

作　　者：张朝霞[1] 海振宏[2] 王华奎[3] 

机构地区：[1]太原理工大学理学院计算机基础教学部,山西太原030024 [2]太原理工天成科技股份有限,公司山西太原030024 [3]太原理工大学信息工程学院,山西太原030024

出　　处：《系统仿真学报》2008年第2期539-541,共3页Journal of System Simulation

基　　金：山西省自然科学基金资助项目(20051038)

摘　　要：模糊系统和神经网络已广泛应用于系统的辨识和控制,但是传统的模糊神经网络是一种静态映射,不适用于动态系统的辨识;而由于无线通信信道的时变性和不确定性,决定了盲均衡器本身就是一个动态的均衡过程,所以研究利用动态递归模糊神经网络的盲均衡算法是可行的,而且也是必要的。仿真结果表明:由于动态模糊神经网络的均衡过程同时利用了系统的当前数据和历史数据,对动态系统的均衡,较传统神经网络在均衡的精度和稳定性方面具有更好的效果。The fuzzy system and neural network are widely used for system identification and control,but the traditional fuzzy neural network is a kind of static map and not suitable to identify the dynamic system.Due to the time-variable and indetermination of mobile channel,blind equalization is a process of dynamic equalization.It’s viable and necessary to use dynamic recurrent fuzzy neural networks to study blind equalization algorithm.The simulation results show that the utilization of the current and past data of the system at the same time makes the presented dynamic recurrent neural network more effective than the traditional fuzzy neural network in view of accuracy and stability for the equalization of the dynamic systems.

关 键 词：动态递归 模糊神经网络 盲均衡 隶属函数 

分 类 号：TN911.5[电子电信—通信与信息系统]