分布式网络控制系统的输出反馈控制

建立了同时具有前向通道传输时延和反馈通道传输时延的线性分布式网络控制系统的数学模型.用该数学模型将系统变换为具有状态与控制时变时滞的线性时滞系统.基于代数Riccati方程方法,提出了H∞鲁棒输出反馈控制方法,以使系统闭环控制稳定.仿真算例结果表明,在t=2.5 s时,系统状态都达到了0,保证了系统具有较快的零状态响应速度.给出了这类系统的H∞鲁棒输出反馈控制器的综合设计示例.     

交通征稽系统计算机网络的安全性研究

为了确保异地征收和银行代征系统的正常运转，有效防范各类计算机网络运行的安全隐患，针对影响计算机网络安全性的因素，经过认真分析研究，提出了确保计算机网络安全运行的策略。     

遗传算法在逆向物流网络规划设计中的应用

考虑到客户、初始回收点和回收中心3个层次的逆向物流网络,选择合适的回收中心,确定初始回收点个数和回收周期,基于一些基本假设,以最小化总的相关成本建立了1个内嵌两个指派模型的非线性混合整数规划模型,对符号体系和求解思路进行了分析.借助遗传算法,结合具体实例,给出了求解步骤和求解结果分析.     

基于DSP控制的无刷直流电机的网络控制方法的研究

介绍了一种基于DSP控制的无刷直流电机的网络控制方法,给出了无刷直流电机控制系统的网络化控制方案,研究了采用HG633光纤转换器实现PC机与DSP的通讯。     

光网络业务恢复算法研究

光网络拓扑结构的变化破坏网络中端到端的连接业务和业务环．根据原有业务路由和结构改变后的网络结构，从原有业务的正向和反向搜索，找出原有业务路由的正向和反向断点；通过回溯法和路由算法搜索出断点之间的路由，连接正向断点之前的路由、断点之间的路由和反向断点之后的路由实现业务恢复。     

复杂网络理论在船舶电力系统结构脆弱性分析中的应用

文章将复杂网络理论分析方法应用于船舶电力系统结构脆弱性的分析。首先将船舶电网抽象为复杂网络模型,并对该网络模型的结构特征进行计算和分析;然后通过分析节点攻击后的电力系统性能对船舶电网结构脆弱性进行分析。结果表明:船舶电网的脆弱性与网络拓扑结构密切相关,利用复杂网络理论可以有效确定电力网络的关键节点。     

载人潜水器母船网络信息系统设计分析

本文结合我国载人深潜科学考察对母船网络信息系统的需求,阐述了载人潜水器母船网络信息系统的设计原则及总体设计方法,并对各分系统设计与实施进行论述,旨在为我国新建载人潜水器母船网络信息系统建设提供参考。     

Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

Practicability study on the suitability of artificial,neural networks for the approximation of unknown steering torques

For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated.