1.A neural net that uses this rule is known as a perceptron, and this rule is called the perceptron learning rule.
一个使用这个规则的神经网络称为感知器，并且这个规则被称为感知器学习规则。
2.neural network is deeply researched as representation of eager classification . perceptron is selected.
选取神经网络分类算法作为急切分类算法的代表进行深入的研究。
3.We conclude that the perceptron act not only as a classifier, it performs classifier with gradient feature.
因此，单层感知机不只做单纯的分类，它能做有层次的分类。
4.A perceptron utilizes weights in a different and perhaps more intuitive way.
感知器以一种不同的而且可能更为直观的方式来使用权重。
5.Perceptron is a kind of useful neural network model and can classify the classification of the detachable linearity correctly.
感知器是一种有用的神经网络模型，可以对线性可分的模式进行正确分类。
6.The Configuration of the Perceptron Introduction to Neural Network No.
第一节感知器的体系结构
7.Perceptron Working Algorithms Introduction to Neural Network No.
第二节感知器工作算法
8.This paper introduces a fuzzy classification model based on the proposed fuzzy kernel hyperball perceptron(FKHP) learning method.
本文提出一种模糊核超球感知器（FKHP）学习方法，并介绍了一种基于FKHP这种学习方法的模糊分类模型。
9.There are important differences from the perceptron algorithm.
这里有一些与感知器算法相区别的重要不同点。
10.Perceptron Introduction to Neural Network No.
神经网络控制篇第六章感知器