1)semantic neural network语义神经网络
1.Deep-seated Semantic Computing Based on Semantic Neural Network;基于语义神经网络的深层语义的计算
2.Studying the Construction of Chinese Surface Semantic Neural Network Based on Ontology;基于本体的汉语表层语义神经网络的构造研究
3.This paper puts forward a new methodology for parsing the surface semantics of the Chinese language based on semantic neural networks,which combines symbolism and connectionism.本文提出一种基于语义神经网络的汉语表层语义分析方法。
英文短句/例句
1.Deep-seated Semantic Computing Based on Semantic Neural Network;基于语义神经网络的深层语义的计算
2.Parsing the Surface Semantics of the Chinese Language Based on Semantic Neural Networks基于语义神经网络的汉语表层语义分析
3.Studying the Construction of Chinese Surface Semantic Neural Network Based on Ontology;基于本体的汉语表层语义神经网络的构造研究
4.Research on a Quantum Genetic Algorithm Based Text Feature Selection Approach基于语义神经网络的文本特征选择方法的研究
5.Neurolinguistics and constructivism network-based multimedia foreign language teaching;神经语言学与建构主义网络化多媒体外语教学
6.Research of Semantic Integration in Heterogeneous Database Based on Nerual Network基于神经网络的异构数据库语义集成的研究
7.Research on Emotional Semantic Distilling基于因子分析和概率神经网络的情感语义提取
8.A Model of Word Sense Disambiguation of English Modal Verb May by a Neural Network基于人工神经网络构建英语情态动词may的语义排歧模型
9.Study in Semantic Landscape Image Retrieval Technology Based on BP Neural Network基于BP神经网络的语义风景图像检索技术的研究
10.Neural Network Generalized Predictive Control for Boiler Combustion System;神经网络广义预测锅炉燃烧控制研究
11.Characteristics Analysis and Improvements of Generalized Congruence Neural Networks;广义同余神经网络的性能分析与改进
12.INVESTIGATION ON NEURAL NETWORKS MODEL IN MAN--TO--COMPUTER SPEECH INTERFACES汉语人机接口中神经网络模型的研究
13.Whispered Speech Enhancement Algorithm Based on BP Neural Networks;基于BP神经网络的耳语音增强的研究
14.Study on Speech Compression Algorithm Based on Wavelet and Neural Networks;基于小波与神经网络的语音算法研究
15.Speech Recognition Based on RBF Neural Network;基于RBF神经网络的语音识别研究
16.Research of Speech Recognition Based on CDHMM/SOFM Neural Network;基于CDHMM/SOFM神经网络的语音识别研究
17.A Research of the Speech Recognition Based on the SOM Model;基于自组织神经网络的语音识别研究
18.The Research of the System of Speech Enhancement Based on BP Neural Network;基于BP神经网络的语音增强系统研究
相关短句/例句
word-semantics BM neural network词―语义BM神经网络
3)generalized neural networks广义神经网络
1.New sufficient conditions of globally exponential stability of generalized neural networks with time delays were presented by using Liapunov algorithm,linear matrix inequality and integral inequality.对于具有时滞的广义神经网络,利用Liapunov函数方法、线性矩阵不等式以及积分不等式等技巧,给出了该神经网络模型的平衡点的存在性、惟一性以及全局指数稳定的一些充分条件。
4)general regression neural network广义神经网络
1.A kind of smoothing factor,which optimizes general regression neural network (GRNN) by improved particle swarm optimization (PSO),is put forward and a method to forecast system marginal price by GRNN with optimized parameters is proposed.提出了一种利用改进粒子群算法优化广义神经网络的平滑因子,并采用优化后的网络预测系统边际价格的方法,该方法克服了利用梯度下降法优化平滑因子时易陷入局部极值点以及利用遗传算法优化平滑因子时收敛速度慢等缺点。
5)generalized neural network广义神经网络
1.Research of generalized neural network and it′s application to traffic flow prediction;广义神经网络的研究及其在交通流预测中的应用
2.Grid parallel computation of online traffic status prediction using generalized neural network在线广义神经网络交通状态预测的网格并行算法
6)Generalized CMAC Neural Network广义CMAC神经网络
1.Generalized CMAC Neural Network and Its Application in Air-Fuel Ratio Control;广义CMAC神经网络及在空燃比控制中的应用
延伸阅读
语义网络理论 用于表示词与词之间的语义关系的一种网络理论。1973年由美国人工智能专家司马贺提出。其原理是以句中词的概念为网络的结点，以沟通结点之间的有向弧来表示概念与概念之间的语义关系，构成一个彼此相连的网络，以理解自然语言句子的语义。 例如， John saw Mary dancing（约翰看到玛丽跳舞。）这个句子，可用下面的语义网络符号来表示： C1 TOKEN(see) TIME PAST DATIVEC2 OBJECT C3 C2 TOKEN(John) NUMBER SINGULAR C3 TOKEN (dancing) TIME PROGRESSIVE PAST AGENT C4 C4 TOKEN(Mary) NUMBER SINGULAR 这里,C1，C2，C3，C4是语义网络中表示概念的结点。see的意义项是C1,John的意义项是C2, dancing的意义项是C3,Mary的意义项是C4。TOKEN表示词项, TIME表示时态，NUMBER表示数,PAST表示过去时, PROGRESSIVE表示进行时，SINGULAR 表示单数， AGENT 表示主体格，DATIVE表示给予格，OBJECT表示客体格，它们都是深层格。根据这样的语义关系，这个句子可用语义网络表示如： 采用语义网络来理解自然语言时，首先分解输入句的句法关系，同时分析句子的深层格结构，记录语义关系，最后求出输入句的语义网络，借以理解自然语言的语义。
