基于一种杂交学习算法的自适应复信道均衡技术

An adaptive complex communication channel equalization method based on a hybrid learning algorithm

本文提出了一种基于多层前馈神经网络杂交学习算法的自适应复信道均衡的新方法。该学习算法用来训练一个输入、输出、权值和激活函数均为复数的神经网络。神经网络的训练利用了监督和非监督相结合的杂交技术 ,而权值的调整是基于TLS(totalleastsquare)准则进行的。计算机仿真结果表明 ,无论是在线性还是在非线性信道中 ,所提出的方法都表现出了很好的性能 ,这为自适应复信道均衡提供了一种新方法。

A novel method for adaptive communication channel equalization based on a multi layer feed forward neural network training algorithm was proposed in this paper.It trains a complex neural network whose inputs,outputs,weights and active functions are all complex valued.The training of the neural network is based on the combination of supervised and unsupervised learning process while the update of the weights based on the TLS (total least square)criterion.Computer simulation results demonstrate that the proposed equalizer has powerful properties both in linear and nonlinear channels.It proposed a novel method for adaptive complex channel equalization.