Multilayer perceptron networks have been widely used in many applications.
多层前传神经网络在许多领域有着广泛的应用。
After some research I have chosen a multilayer perceptron and standard back-propagation algorithm for training.
经过我选择了多层感知和标准的反向传播训练算法的研究。
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 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。
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。
A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.
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。
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。
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。
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 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。
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