一种改进的递归神经网络盲均衡算法

提出了一种新的基于递归神经网络的快速收敛盲均衡算法。设计中采用观测信号的四阶统计量构造代价函数，简化了系统的复杂度；利用实时递归学习算法对系统参数进行动态调节。该算法具有镇定性，其收敛性能不会受到失真信道的影响，适用于均衡衰落性严重的信道。实验仿真结果表明对具有频率选择性衰落的非线性信道，该算法在收敛速度和对抗码间串扰方面都具有良好的性能。

A novel fast convergence blind equalization algorithm based on recurrent neural network is proposed. Four-order statistics of the observation signals are used to calculate the cost function in order to simplify the complexity of the equalization system. Real-time recursion training algorithm is used to dynamically adjust the system parameters. The blind equalization algorithm is “equanimous” and the characteristic of convergence is not influenced by distortion of channel, it is fit for equalizing deep attenuation channel. Simulation results show that the algorithm has good performance on convergence speed and compensating for inter-symbol interference created by multi-path within non-linear channel.