尝试提出了变步长双模式恒模算法，并与神经网络结合应用于盲均衡研究中。
This paper presents variable step dual model constant modulus algorithm, which combine with neural network for blind equalization in nonlinear channel.
理论分析和仿真结果表明改进的变步长线性约束恒模算法分别从收敛性能和稳态性能方面得到了改善。
Theoretical analysis and computer simulation show the variable step LCMA has been improved in both aspects of convergence performance and stability performance.
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