基于RBF神经网络的盲均衡算法综述

Blind Equalization Algorithms Summarization Based on RBF Neural Network

RBF神经网络是一种局部逼近的多层前向神经网络,具有算法简单,收敛快,逼近效果好,泛化能力强等优点。RBF通过非线性基函数的线性组合实现从输入空间到输出空间的非线性转换。盲均衡则是一种不借助训练序列,仅利用接收序列和发送序列的先验知识来恢复发送序列的方法。文中基于RBF神经网络的研究给出盲均衡的算法综述。

RBF is a part imminent multilayer forward neural network. It has simple arithmetic, rapid convergent speed, good imminent result, better extensive ability. RBF can achieve nonlinear conversion from input space to output space by linear combination of nonlinear basis function. Blind equalization doesn＇t utilize a training sequence but just uses the receiving sequence and the prior information of the source sequence to restore the source sequence. This dissertation mainly focuses on the RBF neutral network blind equaiization algorithms summarization.