类神经网络的英文

• articificial neural network
• artificial neural network (ann)

• "神经"英文翻译    nerve; nervus
• "网络"英文翻译    network; electric network me ...
• "类神经网路" 英文翻译 :    artificial neural network; neural network
• "自然语言类神经网络模型" 英文翻译 :    connectionist model for natural language
• "类神经网路模型" 英文翻译 :    connectionist model
• "基于模糊聚类神经网络的镜头突变检测算法" 英文翻译 :    algorithm of abrupt shot boundary detection based on fuzzy clustering neural network
• "mp神经网络" 英文翻译 :    mp neural network
• "光神经网络" 英文翻译 :    optical neural network
• "神经网络" 英文翻译 :    biological neural network; nerve network; neural network, nn; neural networks; neuron network; neuronic network; neutral networks; nn neural network; radial basis function
• "神经网络的" 英文翻译 :    neural net
• "自然语言类神经网路模型" 英文翻译 :    connectionist model for natural language
• "倒转神经网络" 英文翻译 :    back-propagation neutral network
• "递归神经网络" 英文翻译 :    rnn recurrent neural network
• "电子神经网络" 英文翻译 :    electronic neuron network
• "多层神经网络" 英文翻译 :    multilayer neural network
• "蜂窝神经网络" 英文翻译 :    cnn cellular neural network
• "概率神经网络" 英文翻译 :    pnn probabilistic neural network
• "高阶神经网络" 英文翻译 :    higher order neural network
• "混合神经网络" 英文翻译 :    hnn hybrid neural network
• "混沌神经网络" 英文翻译 :    chaotic neural network
• "联想神经网络" 英文翻译 :    aann
• "模糊神经网络" 英文翻译 :    fnn fuzzy neural network; fuzzy neural networks; fuzzy-neural network; rfnn
• "人工神经网络" 英文翻译 :    ann artificial neural network; artificial neural nets (ann); artificial neural network
• "神经网络技术" 英文翻译 :    nerual network technique
• "神经网络模拟" 英文翻译 :    simulation of neural network

• Kohonen clustering neural network based on data field
  聚类神经网络
• Connectionist model for natural language
  自然语言类神经网络模型
• Fuzzy clustering neural network system based on forecast of chemical process
  基于化工过程预报的模糊聚类神经网络系统
• Experiments were conducted and the results shown that the proposed method can detect and recognize different kinds of faults in rotating machinery
  并探讨不同故障状况下盒维数的变化，并结合径向类神经网络来达到故障模式的识别。
• In this paper , we use bp and rbf neural network to build system model separately , and compare the two neural networks to find the difference between them
  文中首先利用bp和rbf神经网络分别对心电信号进行了建模,并比较了这两类神经网络在信号建模方面的优缺点。
• The purpose of this study is to develop a computer model combined with grey relation and two set of neural network to analyze and integrate well logging data to obtain formation parameters around the well
  摘要本研究是利用灰关联分析与两组独立类神经网络相互结合发展出一套自动选定与集成不同类型的井下电测资料，以进行地层参数预测分析的模式。
• In this study , fruit sorting by using fuzzy theory and error - back - propagation ( ebp ) neural network was investigated , the convergence rate and sorting accuracy were discussed ; the performance of ebp neural and fuzzy - neural networks were evaluated
  摘要本研究之主要目的在于利用模糊理论及倒传递类神经网络仿真水果之选别，并探讨不同网络之收敛速度及选别正确率。
• This study employed six data mining methods , including logistic regression , discriminant analysis , artificial neural networks , k - nearest - neighbors , na ? ve bayes classifier , and classification trees , to find the most important factors of earthquake - caused landslide
  本研究利用六种资料探勘方法，包括逻辑回归、判别分析、类神经网络、最近邻法、贝氏分类器、分类树，探讨影响地震引起山崩的重要因子。
• The significance , the function and the development of texture segmentation are discussed in this paper . the statistical and the space / frequency methods of feature extraction , segmentation methods based on fuzzy clustering neural networks are discussed also
  本论文全面论述了纹理图像分割的意义、作用及国内外技术发展概况，介绍了基于统计和空间频域的纹理特征提取方法以及基于模糊聚类神经网络的分割算法。
• The basic thought is to divide the cities which are close to each other into a group ( physical area ) by applying sorting neural network , find out the optimal path by the improved hnn , and then calculate the local optimal path by using the same method , and finally get the whole optimal path , which are described as following : a assembly s of cities is grouped into some subsets according to their physical location and we can get , and then get the optimal , path of tsp of s = { s , i = 1 , 2 , n } through the given method , as well as the st
  基本思想是利用聚类神经网络先把地理位置上相互靠近的城市划分为一个集体单位(一个物理区域) ，用改进的hopfield神经网络算法求解各个区域间的最优(或近似最优)路径，然后再在每一个区域内部用同样的方法来求解其局部的最优(或近似最优)路径，这样可以最终得到全局的最优(或近似最优)解。描述如下：设有城市集合s ，按城市的地理位置把s划分为若干子摘要2集，得s ijs ， ，其中厂s ； d ， i一口求得集合i叫s s ； i习， 2 ， … n的tsp最优路径，再依次求得子集s ；内部的tsp最优路径，即得最终优化路径m一） s一） …一宁s ； di