Perceptron as Feature Detector. Visual Receptive Fields.
做为特征探测器的感应机。视觉的接受域。
The perceptron can only solve linearly separable problems.
感知机只能解决线性可分问题。
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。
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；
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。
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！
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。
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。
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。
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。
The multi-layer perceptron is introduced to charcacterize the microstrip discontinuity by describings-parameters.
本文采用多层感知器建立了微带不连续性的神经网络模型。
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）。
Because liner models have some defects, I construct perceptron model and BP model on base of neural networks theory.
考虑到线性模型的一些缺点，本文随后应用神经网络理论，分别建立感知器预警模型和BP网络预警模型。
Numerical experiments show that the NNKBN model has many advantages over the conventional multi-layer perceptron model.
数值实验表明NNKBN模型在许多方面优于传统的多层感知器模型。
But what the example illustrates is how a perceptron can weigh up different kinds of evidence in order to make decisions.
不过这个例子说明的是感知器如何权衡不同种类因素来做出决策的。 并且一个由感知器组成的复杂网络似乎真的可以做出精准的决定。
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。
The continually optimized connecting relation is gained via perceptron and XOR function, then the optimal path graph is found.
利用感知器异或函数获得了节点之间不断优化的连接关系，然后得到最优路径图。
For modeling of medical ward based on data fusion and data mining, multi - layer perceptron network and decision trees are used.
在结合数据融合和数据挖掘的医疗监护模型的建模方面，采用多层感知器网络和决策树方法建立报警决策器的模型。
The sensitivity analysis approach for the hardware implementation of multilayer perceptron prior to network training is proposed.
提出了训练前多层感知器硬件设计的灵敏度分析方法。
The Problem of Credit Assignment. Perceptron Learning Rule. Convergence Theorem. Learning by Gradient Following. Online learning.
原因探究、感应机学习规则、收敛定理。梯度跟随学习法、线上学习。
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。
The separating system consists of a multilayer perceptron (nonlinear part) followed by a linear blind deconvolution (linear part).
分离系统由多层感知器(非线性部分)后接一个线性盲解卷过程(线性部分)组成。
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。
Of great interest, popular multilayer perceptron (MLP), radial basis function (RBF) and polynomial neural networks are the focus of the paper.
其中，对于多层感知器网络、径向基函数网络、多项式网络尤其关注。
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。
The extracted initial rules and their accuracy and coverage are used to configure the fuzzy perceptron structure and initial weights for training.
网络的结构由已经抽取的规则映射而成，初始连接权由规则的精确度和覆盖度确定。
It is applicable to any small vocabulary hybrid speech recognition system that combines hidden Markov model (HMM) with multi-layer perceptron (MLP).
研究适用于隐马尔可夫模型(HMM)结合多层感知器(mlp)的小词汇量混合语音识别系统的一种简化神经网络结构。
For a neuro_fuzzy classifier based on the fuzzy perceptron, this paper analyses how membership function constraints affect the classification result.
针对一类基于模糊感知器的神经模糊分类器，分析了隶属函数限制条件对分类结果的影响。
The perceptron training rule is based on the idea that weight modification is best determined by some fraction of the difference between target and output.
感知器培训规则是基于这样一种思路—权系数的调整是由目标和输出的差分方程表达式决定。
The perceptron training rule is based on the idea that weight modification is best determined by some fraction of the difference between target and output.
感知器培训规则是基于这样一种思路—权系数的调整是由目标和输出的差分方程表达式决定。
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