regression n. 1.复归,回归。 2.退步,退化。 3.【天文学】退行。 adj. -sive ,-sively adv.
ridge n. 1.【动物;动物学】脊;脊背。 2.山脊;岭,岗;分水岭,山脉。 3.屋脊;(犁沟与犁沟间的)犁垄;鼻梁;隆起线;【筑城】斜堤脊;【铸造】沟,注沟;(气象圈上)狭长的高压带(脊)。 vt. 装屋脊;作垄,培土;使(面上)起皱纹;种在垄上。 vi. 成垄;起皱纹。 n. 里奇〔姓氏〕。
The main research contents include : 1 、 this paper constructs the mixed collaborative sale forecasting model based on cpfr via integrating time series forecasting , multivariate regression and ridge regression . in addition , the model takes sale information as explanation variable 具体研究内容包括: 1 、将时间序列预测、多元回归、岭回归相结合,并将销售信息作为销售量的解释变量,构建了cpfr流程下的混合协同预测模型。
In this paper , an integral scheme of 16 position error calibration and autonomous alignment for three axis platform is given . it may calibrate 33 errors in all . first , determine parameters with least square estimate , then bayes method , ridge regression estimation were discussed separately 本文设计了一个十六位置误差标定方案,可以分离出总计33项误差,首先用最小二乘估计方法进行参数辨识,而后,分别研究了基于bayes方法的误差系数辨识,基于岭估计的误差系数辨识。
However , there have still some unresolved problems : first , how to determine the number and size of the clusters automatically during the clustering process . second , how to utilize the " local " ridge regression method which including multiple regularization parameters in learning rbf network . third , those clusters in irregular form ca n ' t represented by radial basis function , thus we must find some other basis functions that can describe the irregular form 但是仍然存在几个问题尚待解决:首先,聚类时怎样自动确定簇的个数和半径;其次,如何利用含有多个正规化参数的局部岭回归方法进行rbf网络学习;第三,如果簇的形状是不规则的,则它很难用径向基函数来描述,因此需要研究其它能代表不规则形状的簇的基函数。
Define a radial basis function ( rbf ) at center of each cluster and learning a two - layer neural network which consists of these rbfs , simultaneously , for the purpose of avoiding over - fitting , we make use of ridge regression method , which adding a weight penalty term including a appropriate regularization parameter on the cost function and then lead to a more smooth function 为每一个簇的中心定义相应的径向基函数( radialbasisfunction , rbf ) ,再对这些径向基函数构成的两层神经网络进行训练,同时,为了避免产生过度拟合现象,本文采用了岭回归技术,即在代价函数中加入一个包含适当正规化参数的权值惩罚项,从而保证网络输出函数具有一定的平滑度。