识别网络 meaning in English
recognition network
Examples
- On the base of analysis the specialty of distribute system , put forward memory the data with structure array , it is easiness to identify the structure of network and calculate
在分析配电网络特点的基础上，提出采用结构数组存储网络数据，便于识别网络结构和潮流计算，说明了识别网络结构的方法。 - In this paper the measurement of conductive emi emission is discussed , and the design of cm / dm discrimination network is also presented , further , the performance of insertion loss ( il ) and common - mode rejection ( cmr ) is researched , together with the test approach and experiment results
摘要分析了传导性电磁干扰信号的测量方法，提出基于共模差模( cm / dm )信号的识别网络设计，进一步研究了模态识别网络的插入损耗( il )及共模抑制比( cmr )等重要性能，并给出模态识别网络的性能实验设计与分析方法。 - By the application of the rough set theory , this paper makes quantized evaluation with the geological engineering model for slope , explains its establishing method and procedure , and presents a network for the recognition of the deformation and failure modes of slope . examples from practice are analyzed with the model , which concludes its advantages of high speed , precision , practicability and powerful quantization , exploring a new way for quantized model establishment for slope
应用rs ( roughset或粗糙集)理论，对边坡地质工程模型进行量化判别，阐述了rs边坡地质工程模型的建模方法及实现步骤，提出了边坡变形破坏模式的识别网络，进行了实例分析，结果表明该方法具有速度快、精度高、实用性强和量化功能强等优点，为边坡量化建模提供了新的思路。 - Firstly , e - hmm is used to parameterize face image , the output likelihood of the e - hmm is encoded to form the input vector and is sent to the ann . by taking advantage of the discriminative training of ann , the weak discrimination of the maximum likelihood criterion can be improved , and the recognition performance can be improved by means of the learning ability of ann
该混合识别网络用e - hmm的参数来描述人脸的整体性和局部细节性特征，用e - hmm的输出似然值序列组成ann的输入矢量，利用ann的鉴别训练能力来克服e - hmm的基于最大似然准则训练算法区分力较差的弱点，同时利用ann的学习能力来提高e hmm的识别性能。 - In the training this network ' s topology structure and mapping function are the most optimal all along . and the learning velocity and precision are improved . with this recognition method shape pattern information and magnitude can be received rapidly and exactly , which can provide reliable data for later shape controlling
本文将优化网络用于板形信号的模式识别，建立了6输入、 3输出的识别网络模型，该网络性能在训练过程中始终保持最优，能够达到最佳结构，加快了学习速度和训练精度，可以快速、准确求出板形缺陷的模式信息及数值大小，为后续板形控制调节量的设定提供了可靠依据。