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。
In this paper, we using a kind of comprehensive image retrieval which fuses color and texture features by linear weights and discuss the method which the weights are determined.
在检索中，颜色和纹理特征的权重不同，本文采用线性加权方式综合颜色特征相似距离和纹理特征相似距离，对图像进行检索。
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。
In this scheme, the inputs of hidden layer neurons are acquired by using the gradient descent method, and the weights and threshold of each neuron are trained using the linear least square method.
在该方案中，通过梯度法获取隐层神经元的输入，使用线性最小二乘法训练各神经元的权值和阈值。
The Generalized Hamming Weights (GHW) of linear codes characterize the cryptography performance of the code on the wire-tap channel of type two.
线性码的广义汉明重量谱描述了码在第二类窃密信道中传输的密码学特征。
The Generalized Hamming Weights (GHW) of linear codes characterize the cryptography performance of the code on the wire-tap channel of type two.
线性码的广义汉明重量谱描述了码在第二类窃密信道中传输的密码学特征。
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