提出了一種新的基于遞歸神經網絡的快速收斂盲均衡算法。設計中采用觀測信號的四階統計量構造代價函數，簡化了系統的復雜度；利用實時遞歸學習算法對系統參數進行動態調節。該算法具有鎮定性，其收斂性能不會受到失真信道的影響，適用于均衡衰落性嚴重的信道。實驗仿真結果表明對具有頻率選擇性衰落的非線性信道，該算法在收斂速度和對抗碼間串擾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.