均衡器自适应算法有最小均方误差算法（LMS-lowest mean square error）；递归最小 二乘法（RLS-recursive least square）；快速卡尔曼（fast Kalman）等。
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虽然判决导引最小均方误差算法(DD - LMS)剩余误差小，但是，在信道眼图闭合的情况下，该算法无法收敛，即不具备冷启动能力。
Though the decision directed least mean square error algorithm (DD-LMS) has small residual error, when the channel eye close, it can not converge. That is, it has no cold start ability.
实验表明该方法融合结果的均方误差比拉普拉斯金字塔算法和小波变换方法降低约30% - 60%。
The experimental results demonstrate that the MSE (mean square error) reduced by this proposed approach decreases 30% - 60% than that by Laplacian pyramid and discrete wavelet transform approaches.
此算法相对于均方误差最小准则和最大误差最小化准则具有占用存储空间小和计算时间短的优点。
Compared with the minimum mean squared error principle and the minimaxs principle this one needs smaller memory space and less computation time.
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