ID 原文 译文
9564 多样化的装备体系定义模式存在概念语义内涵交织、外延对象重叠难以离析,体系系统边界动态模糊等不确定性制约装备体系综合认知,大数据建模与分析决策为装备体系模型表达与知识发现提供了机遇。 Diversified equipment system definition model concept semantic connotation and denotation object overlap to segregation, the dynamic system of the boundaries of the system fuzzy comprehensive cognitive uncertainty restriction and equipment system, such as large data modeling and analysis of decision making for equipment system model expression and the knowledge discovery provides the opportunity.
9565 提出广义复杂信息系统观点下采取形式三元概念分析理论描述和表示装备体系中概念及概念之间层次网络结构的装备体系认知系统模型构造框架。 Put forward the general concept to form ternary complex information system analysis theory to describe and said gear system concept and concept of the hierarchical network structure between equipment system cognitive system model structure framework.
9566 首先分析了能力视角下装备体系语义超网络模型的大数据描述架构及其层次建模过程,设计了一种体系分层共演化时空数据应用模型结构。 Ability perspective equipment system is firstly analyzed the semantic network model of big data describing architecture and its hierarchical modeling process, we design a system hierarchical model structure evolution spatio-temporal data application.
9567 概述了概念认知系统研究进展,提出一种基于语义上下文的形式三元概念构造策略,讨论了上下文覆盖数据结构与三元概念构造的联系。 Outlines the concept of cognitive system research progress, this paper puts forward a concept of form ternary structure strategy based on semantic context, discusses the context data structure to the concept of the ternary structure.
9568 为应用语义大数据开展装备体系认知计算提供了有益启发。 For the application of semantic big data to carry out the cognitive computing equipment system provides beneficial inspiration.
9569 针对批处理方法在实现非等功率同步直接序列码分多址(direct sequence code-division multiple access,DS-CDMA)信号伪码序列盲估计时存在的复杂度高、收敛速度慢的问题,引入了3种多主分量神经网络(Sanger NN、LEAP NN和APEX NN)。 For batch processing method in the implementation of power, such as synchronization of direct sequence code division multiple access (direct sequence code - division multiple access, DS - CDMA) signals of blind estimation of pn code sequence complexity is high, the problems of slow convergence speed, introduced more than three principal component neural network (Sanger NN, LEAP NN and APEX NN).
9570 首先将已分段的一周期DS-CDMA信号作为神经网络的输入信号,用神经网络各权值向量的符号函数代表DS-CDMA信号各用户的伪码序列。 ‭Will first a cycle has been segmented DS - CDMA signal as the input signal, using neural network function of each weight vector symbols represent DS - CDMA signal the pn code sequence of each user.
9571 然后通过不断输入信号来反复训练权值向量直至收敛,最终DS-CDMA信号各用户的伪码序列就可以通过各权值向量的符号函数重建出来。 And then through continuous input signal training weight vector over and over until convergence, eventually DS - CDMA signal the pn code sequence of each user can through the symbols of each weight vector function reconstruction.
9572 此外,本文提出了一种在递归最小二乘(recursive least square,RLS)意义下的最优变步长收敛模型,极大地提高了网络的收敛速度。 ‭In addition, this paper puts forward a recursive least squares (recursive further square, RLS) under the significance of optimal variable step convergence model, greatly improved the convergence speed of the network.
9573 理论分析与仿真实验表明:将3种神经网络用于同步非等功率DS-CDMA信号伪码盲估计时的复杂度均明显降低。 ‭Theoretical analysis and simulation experiments show that the three kinds of neural network is used to synchronize the pseudo-code power DS - CDMA signals, such as the complexity of the blind estimation were significantly lower.