这里有一些与感知器算法相区别的重要不同点。
There are important differences from the perceptron algorithm.
前者使用了统计规则和长度-标准差模型，后者采用感知器算法及共现模型实现。
The first step is candidate phrase identification, which is based on statistical rules and a Length Standard Deviation measurement.
分析了感知器算法解空间的几何特性，提出了一种基于解区域的感知器改进算法。
This paper presents an improved perception approach based on the soluble region, and puts forward a process by analyzing the geometry characteristic of the perception approach in which solve space.
提出了一种单层感知器网络训练的新算法。
A new algorithm is proposed for training single layered perception neural networks.
导出了便于VLSI实现的多项式感知器的格型实现算法，进行了计算机模拟，并给出了相应的数值结果。
A new lattice polynomial perceptron (LPP) model is derived, which is very suitable for VLSI implementation. Computer simulations have been carried out and the experimental results are given.
提出了一种基于多层感知器网络的曲面重构算法(ML P SR)，建立了用于曲面重构的多层感知器网络模型。
The paper presents a surface reconstruction algorithm based on multi layered perception (MLPSR), and constructs a neural network model of surface reconstruction.
该算法运用多层感知器估计训练样本的分布函数，然后求导得到概率密度。
SLC uses a multiplayer network to estimate the distribution function of the training samples and obtains density by taking derivative.
该算法运用多层感知器估计训练样本的分布函数，然后求导得到概率密度。
SLC uses a multiplayer network to estimate the distribution function of the training samples and obtains density by taking derivative.
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