带有持续扰动的时滞非线性大系统的最优跟踪控制

研究具有外界持续扰动的时滞非线性大系统的无静差最优跟踪控制问题 .将时滞非线性大系统分解为带有互联项的N个时滞非线性子系统,基于内模原理对子系统构造扰动补偿器,将带有外部持续扰动的子系统化为无扰动的增广系统 .通过灵敏度法求解不含时滞的两点边值问题,得到子系统的最优跟踪控制律,截取最优跟踪控制律的前N项作为次优控制律来近似系统的最优控制律. 仿真实例表明了该设计方法的有效性.

     

一类具有通信逻辑的网络控制系统稳定性分析

针对具有线性不确定对象的网络控制系统的带宽约束问题,设计了具有周期通信逻辑的网络控制系统的结构,建立了具有周期通信逻辑的网络控制系统模型. 运用现代控制理论和矩阵摄动理论相关原理,进一步证明了系统保持渐近稳定的充分条件 .最后通过仿真算例验证了结论的有效性.

     

控制增益符号未知的MIMO 时滞系统自适应控制

针对一类带有死区模型并具有未知函数控制增益的不确定MIMO 非线性时滞系统,基于滑模控制原理和Nussbaum函数的性质,提出了一种稳定的自适应神经网络控制方案 .该方案放宽了对函数控制增益上界为未知常数的假设,并通过使用Lyapunov0Krasovski 泛函抵消了因未知时变时滞带来的系统不确定性. 理论分析证明,闭环系统是半全局一致终结有界.仿真结果表明了该方法的有效性.

     

非对称信息下供应链库存系统Pareto 优化模型

运用委托代理理论,研究了非对称信息条件下,由单供应商单分销商组成的供应链 Pareto优化问题. 在假定分销商所面临的需求是与销售价格有关的随机变量,以及供,销双方关于分销商销售价格信息不对称这两个前提下,将供应商作为委托人,分销商作为代理人,给出了供应商为吸引销售商选择对自己最有利的销售价格而设计的最优激励合同,比较了在不同信息条件下供销双方的最优决策 .最后,给出了应用实例和灵敏度分析.

     

Scan Cell Positioning for Boosting the Compression of Fan-Out Networks

Ensuring a high manufacturing test quality of an integrated electronic circuit mandates the application of a large volume test set. Even if the test data can be fit into the memory of an external tester, the consequent increase in test application time reflects into elevated production costs. Test data compression solutions have been proposed to address the test time and data volume problem by storing and delivering the test data in a compressed format, and subsequently by expanding the data on-chip. In this paper, we propose a scan cell positioning methodology that accompanies a compression technique in order to boost the compression ratio, and squash the test data even further. While we present the application of the proposed approach in conjunction with the fan-out based decompression architecture, this approach can be extended for application along with other compression solutions as well. The experimental results also confirm the compression enhancement of the proposed methodology.     

Ubiquitous Mining with Interactive Data Mining Agents

Due to the increasing availability and sophistication of data recording techniques, multiple information sources and distributed computing are becoming the important trends of modern information systems. Many applications such as security informatics and social computing require a ubiquitous data analysis platform so that decisions can be made rapidly under distributed and dynamic system environments. Although data mining has now been popularly used to achieve such goals, building a data mining system is, however, a nontrivial task, which may require a complete understanding on numerous data mining techniques as well as solid programming skills. Employing agent techniques for data analysis thus becomes increasingly important, especially for users not familiar with engineering and computational sciences, to implement an effective ubiquitous mining platform. Such data mining agents should, in practice, be intelligent, complete, and compact. In this paper, we present an interactive data mining agent — OIDM (online interactive data mining), which provides three categories (classification, association analysis, and clustering) of data mining tools, and interacts with the user to facilitate the mining process. The interactive mining is accomplished through interviewing the user about the data mining task to gain efficient and intelligent data mining control. OIDM can help users find appropriate mining algorithms, refine and compare the mining process, and finally achieve the best mining results. Such interactive data mining agent techniques provide alternative solutions to rapidly deploy data mining techniques to broader areas of data intelligence and knowledge informatics.     

Logic Programs,Compatibility and Forward Chaining Construction

Logic programming under the stable model semantics is proposed as a non-monotonic language for knowledge representation and reasoning in artificial intelligence. In this paper, we explore and extend the notion of compatibility and the Λ operator, which were first proposed by Zhang to characterize default theories. First, we present a new characterization of stable models of a logic program and show that an extended notion of compatibility can characterize stable submodels. We further propose the notion of weak auto-compatibility which characterizes the Normal Forward Chaining Construction proposed by Marek, Nerode and Remmel. Previously, this construction was only known to construct the stable models of FC-normal logic programs, which turn out to be a proper subclass of weakly auto-compatible logic programs. We investigate the properties and complexity issues for weakly auto-compatible logic programs and compare them with some subclasses of logic programs.     

H-Trust: A Group Trust Management System for Peer-to-Peer Desktop Grid

Peer-to-Peer Desktop Grid (P2PDG) has emerged as a pervasive cyber-infrastructure tackling many large-scale applications with high impacts. As a burgeoning research area, P2PDG can support numerous applications, including scientific computing, file sharing, web services, and virtual organization for collaborative activities and projects. To handle trustworthiness issues of these services, trust and reputation schemes are proposed to establish trust among peers in P2PDG. In this paper, we propose a robust group trust management system, called H-Trust, inspired by the H-index aggregation technique. Leveraging the robustness of the H-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust personalized reputation evaluation mechanism for both individual and group trusts with minimal communication and computation overheads. We present the H-Trust scheme in five phases, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase, and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm are further investigated. To validate the performance of H-Trust scheme, we designed the H-Trust simulator HTrust-Sim to conduct multi-agent-based simulations. Simulation results demonstrate that H-Trust is robust and can identify and isolate malicious peers in large scale systems even when a large portion of peers are malicious.     

直觉模糊集隶属度与非隶属度函数的确定方法

基于证据理论研究直觉模糊集隶属度和非隶属度函数的确定问题是一种新的思路.首先分析信任函数,似然函数与隶属度函数,非隶属度函数的互通性;然后给出广义基本概率分配(BPA)函数,广义信任函数和广义似然函数的定义;最后在这3个改进定义的基础上建立直觉模糊集隶属度函数,非隶属度函数模型,通过证明和实例验证了模型的正确性和有效性.

     

路网辅助的车载INS/GPS组合导航方法

针对车载INS/GPS组合导航系统在GPS无效时精度迅速下降的问题,提出了将车辆行驶的路网约束作为虚拟传感器,采用多传感器数据融合的方式,与INS和GPS组成INS/GPS/路网组合导航系统.当GPS失效时,使用路网辅助INS.仿真结果表明,在GPS无效时间段,该方法能有效减小系统定位误差.