Abstract: In digital communications，the received signal is often corrupted by the inter-symbol interferences（ISI），and the ISI can be eliminated by blind equalization.The constant modulus algorithm is a widely applied blind equalization algorithm.For its slow convergence and big remnant errors，a new blind equalizer based on neural network is proposed.It can make the network convergent by very few training serial signals，and then the equalizer changes to the blind algorithm.The simulations show that this equalizer has less remnant errors and faster convergence speed than the ordinal equalizers，no matter in linear channel or nonlinear channels.

Key words: neural network, blind equalization, constant modulus algorithm

摘要： 在数字通信中，接收信号通常会受到码间干扰的影响。采用盲均衡技术可以消除码间干扰，常模算法（CMA）是应用较广泛的盲均衡算法。因基于常模算法的盲均衡器存在收敛速度慢，剩余误差大的缺点，提出了一种新的基于神经网络的CMA盲均衡器。通过很少的训练序列使网络收敛，再转入盲均衡算法。实验仿真表明，无论是在线性信道还是非线性信道，该均衡器的剩余误差都比普通CMA均衡器较小，收敛速度也较快。

关键词: 神经网络, 盲均衡, 常模算法