在径向基函数神经网络中，隐层中心的数量和位置的选择是整个网络性能优劣的关键，直接影响网络的分类能力。
The choice of quantity and position of hidden layer radial basis functions is very important and directly affects the goodness of fit of overall network classification ability.
对原始数据和影像的直接访问具有转换分类学被实践的方式的能力，并且其结果传播到了公众社区。
Direct access to primary data and imagery has the power to transform the means by which taxonomy is practiced and its results disseminated to the general community.
分类器设计是模式识别系统中的关键步骤之一，它直接影响到系统的分类能力。
The classifier design is a key step for pattern recognition systems, which can effect performance of system.
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