The multi-layer perceptron is introduced to charcacterize the microstrip discontinuity by describings-parameters.
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。
Numerical experiments show that the NNKBN model has many advantages over the conventional multi-layer perceptron model.
数值实验表明NNKBN模型在许多方面优于传统的多层感知器模型。
It is applicable to any small vocabulary hybrid speech recognition system that combines hidden Markov model (HMM) with multi-layer perceptron (MLP).
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。
For modeling of medical ward based on data fusion and data mining, multi - layer perceptron network and decision trees are used.
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。
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.
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.
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。
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