The triangle vector basis function (RWG) is employed to simulate the current distribution.
采用三角矢量面元(rwg)基函数，模拟电流分布。
For this reason, several important properties of kernel function are discussed on the basis of support vector machine.
为此，在研究支持向量机的基础上，给出了核函数的若干重要性质。
In this paper, a novel adaptive control approach based on fuzzy basis function vector is presented for Multi input and Multi output (MIMO) nonlinear systems.
针对多输入-多输出(MIMO)非线性系统基于模糊基函数向量提出了一种新的自适应控制方法。
The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel feature space is constructed.
所接收的混合信号首先被映射到高维的内核特征空间，和由内核特征空间上的适应度函数给出的特征矢量的基础构造。
A classification method based on fuzzy vector space model and radial basis function network is presented in this paper.
文本提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
For reliability analysis of structure with implicit limit state function, an iterative algorithm was presented on the basis of support vector classification machine.
针对结构隐式极限状态函数的可靠性分析，提出了一种支持向量机分类迭代算法。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
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