The study of the generalization ability of Multilayer Perceptrons(MLPs).
①多层感知机推广性能的定量度量。
In this article we investigate logic classification problems that are solved using perceptrons on MATLAB condition.
本文研究在MATLAB环境下，利用感知器求解逻辑分类问题。
We introduce ridge approximation techniques, which include single - and multi-layer perceptrons and projection pursuit regression techniques.
我们要介绍包含单层和多层式认知及投影式追踪回归法在内的脊近似值法。
An improved design method on pattern classifier based on multi-layer perceptrons (MLP) by means of minimum classification error (MCE) training was proposed.
提出了一种基于最小分类错误(MCE)训练的采用多层感知器(MLP)结构的模式分类器设计方法。
Then, a method is presented to compress the number of hidden nodes, which can be extended to more than three layer perceptrons and to the case of using different activation functions.
然后，给出了一种可对上述三层感知器进行压缩的隐节点的压缩方法，它可以推广到三层以上的感知器和节点激发函数不同的情形。
Then, a method is presented to compress the number of hidden nodes, which can be extended to more than three layer perceptrons and to the case of using different activation functions.
然后，给出了一种可对上述三层感知器进行压缩的隐节点的压缩方法，它可以推广到三层以上的感知器和节点激发函数不同的情形。
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