风速的反演是基于多层感知器网络；
The network for retrieving wind speed is a multi-layer perceptron.
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。
Of great interest, popular multilayer perceptron (MLP), radial basis function (RBF) and polynomial neural networks are the focus of the paper.
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。
For modeling of medical ward based on data fusion and data mining, multi - layer perceptron network and decision trees are used.
提出了一种基于多层感知器网络的曲面重构算法(ML P SR)，建立了用于曲面重构的多层感知器网络模型。
The paper presents a surface reconstruction algorithm based on multi layered perception (MLPSR), and constructs a neural network model of surface reconstruction.
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.
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 quintessential example of a deep learning model is the feedforward deepnetwork or multilayer perceptron (MLP).
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.
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.
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).
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.
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.
在将结肠镜图像分类为正常和异常图像时，选用了误差反传训练的多层感知器，结果显示这种网络神经元适合结肠镜图像分类。
The results show that the neural network is more appropriate for the classification of colon status. The new algorithm in this paper has been tested by a number of colonoscopic images.
在将结肠镜图像分类为正常和异常图像时，选用了误差反传训练的多层感知器，结果显示这种网络神经元适合结肠镜图像分类。
The results show that the neural network is more appropriate for the classification of colon status. The new algorithm in this paper has been tested by a number of colonoscopic images.
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