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。
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！
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。
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。
Now, if the true function were Boolean or, then the perceptron would have correctly generalized from three training instances to the full set of instances.
现在，如果true函数是布尔或，那么感知器将从三个训练实例中归纳出所有的实例。
For instance, a certain kind of basic neural network, the perceptron, is biased to learning only linear functions (functions with inputs that can be separated into classifications by drawing a line).
例如，某些基本的神经网络，它们的感知器只倾向于学习线形函数(通过划一条线可以把函数输入解析到分类系统中)。
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）。
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；
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 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。
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。
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。
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。
This paper presented a optimum linear multiuser detector in terms of minimum mean bit error rate, and given a approximate method of solving the detector - method of training single perceptron.
本文提出了平均误比特率最小意义下的最佳线性多用户信号检测器，并给出了求解这种最佳线性多用户信号检测器的近似方法——训练单层感知器法。
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.
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。
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 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。
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。
For multilayer perceptron with single hidden layer, the computer simulation is done to get the number of hidden neurons and quantization bit which satisfy the design requirement.
针对单隐层感知器的硬件设计进行了计算机仿真，得到了满足设计要求的隐层神经元个数和量化比特数。
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。
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 linear forward feedback perceptron with single layer is used to seek the inverse matrix in failure analysis of power system, so that the calculation is simplified.
将单层线性前馈网络用于矩阵求逆的方法，应用于电力系统故障计算中，从而简化了计算。
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 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。
The invention discloses a voltage detection and correction device used for an oxygen-contained perceptron, aiming at improving the accuracy of induction output results.
本发明公开了一种含氧感知器的电压检知导正装置，为提高感应输出结果的准确性而发明。
The invention discloses a voltage detection and correction device used for an oxygen-contained perceptron, aiming at improving the accuracy of induction output results.
本发明公开了一种含氧感知器的电压检知导正装置，为提高感应输出结果的准确性而发明。
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