对线性网络的模型选择和多层前馈网络的关系做了细致的研究，并分别进行了仿真。
A delicate research about the relationship of the selection of model of the linear network part and the multi-layered feed-forward network part and simulation are presented.
本文分析了目前文献中研究较多的基于多层前馈网络的谐波电流检测方法的基本原理。
This paper analyzes the basic principles of the harmonic detection method based on multilayer feedforward neural networks, which has been studied extensively at present.
提出了径流长期分级预报的人工神经网络方法，给出了多层前馈网络的联合梯度算法。
An artificial neural network method is proposed for hydrological forecasting, the hybrid gradient method for multilayer feedforward neural network developed.
为了优化用于故障分类的多层前馈网络结构配置，提出了一种基于模糊逻辑的优化方法。
In order to optimize the design of MLP network structure for fault classification, an optimum approach based on fuzzy logic is presented.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
多层前馈人工神经网络在装备故障诊断中的应用含设备运行状态特征值设定和故障判定。
The application of multi-layer feed-forward artificial neural network in fault equipment diagnosis includes feature value setting of equipment operation condition and fault judgment.
多层前馈神经网络是在实践中应用最为广泛的一种神经网络。
Multilayer feedforward neural network is the most popular one in practice.
给出一种基于多层前馈神经网络的中文文本分类模型，介绍了该模型的设计和实现。
This paper presents a text categorization model based on multilayered feedforward neutral network, and introduces the design and implementation of this model.
本文采用多层前馈神经网络对遥感图像进行压缩，给出了具体的压缩算法。
This paper compresses remote sensing images with multilayer feedforward neural network and gives the compression algorithm in detail.
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).
采用遗传算法训练的多层前馈神经网络实现番茄成熟度的自动判别。
Automated identification of tomato maturation using multilayer feedforward neural network with GA can be realized.
讨论了利用仅含一个隐层的前馈多层神经网络来辨识离散时间非线性动态系统时的模型检验问题。
This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.
提出的自适应粒子群优化算法，用于优化多层前馈神经网络的拓扑结构，提高了神经网络的学习质量和速度。
The structure of multi-layer feedback forward neural network is optimized by improved PSO. Learning quality and training speed of the neural network are improved.
采用多层前馈模糊神经网络——FMLP实现对儿童个体综合素质的定量评价。
The quantitative assessment on children′s comprehensive quality is fulfilled by the fuzzy multi-layer perceptions networks—FMLP.
为了更好满足谐波测量的实时性和测量精度，我们进一步研究了基于多层前馈神经网络在谐波测量中的方法。
In order to improve the real-time and precision, the methods with multi-layer feed-forward neural networks are investigated more.
提出了一种利用多层前馈神经网络生成纹理图象的新方法。
In this paper, a new method to generate the texture image by use of Multi layer feed forward neural network is presented.
用前馈多层神经网络方法研究了高聚物的热力学性质。
The thermodynamical properties of polymers are studied using a BP artificial neural network model.
本文应用多层前馈神经网络和自组织特征映射神经网络分别对简单目标和复杂飞机目标进行了分类识别。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
介绍了系统构成原理和用于废水紫外光谱图识别的多层前向神经网络的设计， 分析了含有不同隐节点数的前馈网络的精度；
The structure of the system and the design of the multiple feedforward neural network used for identification of spectrogram of wastewater are discussed.
也就是说我们只需研究其一维函数逼近能力，所得的结论完全适合于多维情形，该定理大大简化了前馈多层神经网络函数逼近问题的分析难度。
This theorem simplifies greatly the analysis of the function approximation ability of FFMLNN because one needs only to study the one dimensional function approximation ability of FFMLNN.
同时将所建立的模型与以往回归方法建模进行了比较，可以看出多层前馈神经网络要优于回归方法建立的模型。
At the same time, comparing the model with traditional model by regression method, we obtained that the former is better.
然后把分解后的图像作为多层前馈神经网络的输入节点，对自动喷漆线上的汽车车型进行识别。
The decomposed images are then used as the nodes on input-layer of multi-layer feed-forward network, which recognize the vehicle model on painting line.
然后把分解后的图像作为多层前馈神经网络的输入节点，对自动喷漆线上的汽车车型进行识别。
The decomposed images are then used as the nodes on input-layer of multi-layer feed-forward network, which recognize the vehicle model on painting line.
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