修正权 meaning in Chinese
right of amendment
Examples
- A real rough set space and the concepts of real lower and upper approximation corresponding to real - valued attributes is studied . a rhombus neighborhood for som is proposed , and the combination of som and rough sets theory is explored in the dissertation . according to the distance between the weight of winner node and the input vector in the real rough sets space , some new weights learning rules are defined
本文提出采用菱形邻域代替一般的方形邻域,可以减少待修正权重的数目;并利用实数粗糙空间的下、上近似集的精确概念划分自组织映射的输出结果,使得改进后的映射结果中各类样本点之间有明显的间隔,易于进行分类识别。 - In this dissertation , two kinds of optimization , methods are proposed . firstly , only these linking weights corresponding to the control rules that affect the control performance significantly are updated in order to reduce the compute works and speed up the training progress . secondly , the updating step is adjusted adaptively in accordance with the error and the change of error of the system based on the t - s model to get better performance
针对模糊神经网络控制器一般存在着在线权值调整计算量大、训练时间长、过度修正权值可能导致系统剧烈振荡等缺点，提出了两种模糊神经网络控制器的优化方法：在线自学习过程中仅对控制性能影响大的控制规则相关的权值进行修正，以减小计算量，加快训练速度；基于t - s模糊模型，根据偏差及偏差变化率大小动态自适应调节权值修正步长，抑制控制器输出的剧烈变化，避免系统发生剧烈振荡。 - Adding momentum item while correcting weight and limiting range of input value reduce error and improve diagnosis correctness greatly . while normalizing the input value , a new way is put forward that normalization is performed item by item according to its sort . in this way error training can avoid going into the flat field that is caused by existing of 0 or 1 of the input value
本文首先分析了故障诊断和神经网络的基本理论，并在此基础上提出了神经网络对于变压器故障诊断系统的适用性；文中将bp神经网络算法用计算机实现；并针对其本身存在的一些缺点提出了一系列改进措施，通过在修正权值的时候增加动量项，并且限制输入值范围来减小误差、提高系统的诊断正确率；在对输入数据进行归一化处理的时候，采取按类逐项归一化的方法，避免了输入数据出现0或者1而使训练进入平坦区。