Multilayer feedforward neural network is the most popular one in practice.
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。
Some problems in the research of multilayer feedforward network are pointed out.
对当前前向网络研究中的一些问题提出了看法。
A new second order recursive learning algorithm to multilayer feedforward network is proposed.
提出了多层前向神经网络的新型二阶递推学习算法。
Automated identification of tomato maturation using multilayer feedforward neural network with GA can be realized.
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。
In this paper, the back propagation algorithm of a multilayer feedforward neural network was defined as BP algorithm?
利用前向多层神经网络的反向传播算法，即BP算法。
Diagnosis models based on multilayer feedforward neural networks are widely used in the field of machinery fault diagnosis.
基于多层前向网络的诊断模型在设备故障诊断领域应用比较广泛。
The neural network used is called DLF network, which is the combination of multilayer feedforward network with linear model.
所采用的网络为一种将线性模型与多层前向网络相结合的DLF网络。
This paper compresses remote sensing images with multilayer feedforward neural network and gives the compression algorithm in detail.
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。
An artificial neural network method is proposed for hydrological forecasting, the hybrid gradient method for multilayer feedforward neural network developed.
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。
This paper analyzes the basic principles of the harmonic detection method based on multilayer feedforward neural networks, which has been studied extensively at present.
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 presents a new method of identifying the mass of free flying space robot body using multilayer feedforward neural network based on its attitude disturbance characteristic.
根据空间机器人姿态干扰特性，本文提出了利用多层前向神经网络来辨识自由飞行空间机器人本体质量的算法。
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）。
Over-fitting in feedforward multilayer neural network was studied in this paper.
对多层前传网络的过拟合问题进行了探讨。
Over-fitting in feedforward multilayer neural network was studied in this paper.
对多层前传网络的过拟合问题进行了探讨。
应用推荐