..., γ q ，使得Q(X G ) 为p j (X G ) 的线性组合 Q(x G ) = q j=1 γ j p j (x G ) 这些系数我们称为线性权（linear weights），它取决于网格几何特性、点x G 及重构或插值过程的特殊条件。
基于12个网页-相关网页
The main problems in designing a RBFNN depend on fixing the nodes of the hidden layer, the parameters of the centers and the linear weights.
设计中存在的主要问题包括隐层神经元数、中心和半径的确定，以及网络权值的训练。
The parameter of some special linear codes - Period Distribution and Generalized Hamming Weights (Support Weights or Higher Weights) were studied in this paper, and it was departed by two section.
这篇论文主要研究了几类特殊线性码的两个新的参数-周期分布和广义汉明重量(有时也称为高维重量或支持重量)。
The parameters of linear network are identified by recursive least square and weights and thresholds of MFNN are learned by BP algorithm.
线性网络部分的参数采用递推最小二乘法辨识，多层前向网络的权值和阈值采用BP算法学习。
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