在预处理环节，通过对各种归一化算法进行分析，确定了一种基于点密度和线密度相结合的非线性归一化方法。
In preprocess stage, based on analysis of many normalization algorithm, the author propose a novel non-linear normalization method, which is the combination of point-density and line-density method.
为了更有效地提取手写汉字的特征，提高识别精度，本文提出了一种利用非线性归一化过程产生的坐标变换信息来提取手写汉字有效特征的方法。
By using enhanced weighted dynamic meshes based on nonlinear normalization, this method not only avoids the zigzags and other undesirable side effects introduced in the original Yamada et al.
变步长非线性自适应拉格里归一化lms算法。
Variable step size adaptive nonlinear laguerre normalized LMS algorithm.
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