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1·Perceptron as Feature Detector. Visual Receptive Fields.

做为特征探测器的感应机。视觉的接受域。

2·The perceptron can only solve linearly separable problems.

感知机只能解决线性可分问题。

3·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。

4·The network for retrieving wind speed is a multi-layer perceptron.

风速的反演是基于多层感知器网络；

5·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。

6·Obviously, the perceptron isn't a complete model of human decision-making!

显然，感知器不是一个人类决策的完整模型！

7·Multilayer perceptron networks have been widely used in many applications.

多层前传神经网络在许多领域有着广泛的应用。

8·A method of constructing knowledge based fuzzy perceptron based on rough sets theory is proposed.

提出了用粗糙集理论构造模糊多层感知器的方法。

9·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。

10·After some research I have chosen a multilayer perceptron and standard back-propagation algorithm for training.

经过我选择了多层感知和标准的反向传播训练算法的研究。

感知器

1·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。

2·The quintessential example of a deep learning model is the feedforward deepnetwork or multilayer perceptron (MLP).

深度学习模型的一个典型例子是前馈深度网络，或者说多层感知器（MLP）。

3·A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.

介绍一种用循环多层感知器神经网络实现符号逻辑推理系统的方法。

4·Obviously, the perceptron isn't a complete model of human decision-making!

显然，感知器不是一个人类决策的完整模型！

5·In this paper, the authors study the detection of signals in non-Gaussian noise, and employ a multilayer perceptron neural network as a detector.

本文研究了非高斯噪声中信号的检测，采用多层感知器神经网络作为检测器。