提出了一种基于函数联接的感知器神经网络的纹理分类方法。
This paper presents a texture classification approach based on function link network.
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。
A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.
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。
In this paper, the authors study the detection of signals in non-Gaussian noise, and employ a multilayer perceptron neural network as a detector.
将分形计算维数概念与多层感知器神经网络结合，建立了机械设备的分形神经网络诊断方法。
Combining fractal calculating dimension with multi layer neural network, a diagnosis method named as fractal neural network is built.
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)。
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).
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。
A neural net that USES this rule is known as a perceptron, and this rule is called the perceptron learning rule.
本文研究神经网络的多层感知器模型在语音识别中的应用。
This paper describes the use of multi-layer perception model of neural network in speech recognition.
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。
Perceptron is a kind of useful neural network model and can classify the classification of the detachable linearity correctly.
一般对特定的基于多层感知器的故障诊断问题，很难确定神经网络的结构。
Generally, it is difficult to determine in advance a suitable network structure when a multi layer perceptron neural networks is used for a special fault diagnosis problem.
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 multi-layer perceptron is introduced to charcacterize the microstrip discontinuity by describings-parameters.
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。
Because liner models have some defects, I construct perceptron model and BP model on base of neural networks theory.
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。
It is applicable to any small vocabulary hybrid speech recognition system that combines hidden Markov model (HMM) with multi-layer perceptron (MLP).
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
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