Robust H∞ filtering for discrete-time systems with Markovian switching and time-delays

The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with modedependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensures robust stochastic stability while achieving a prescribed H∞ performance level of the resulting filtering error system, for all admissible uncertainties. The key features of the approach include the introduction of a new type of stochastic Lyapunov functional and some free weighting matrix variables. Sufficient conditions for the solvability of this problem are obtained in terms of a set of linear matrix inequalities. Numerical examples are provided to demonstrate the reduced conservatism of the proposed approach.     

A multiyear lags input-holding-output model on education with excluding idle capital

This paper develops a multi-year lag Input-Holding-Output (I-H-O) Model on education with exclusion of the idle capital to address the reasonable education structure in support of a sustainable development strategy in China. First, the model considers the multiyear lag of human capital because the lag time of human capital is even longer and more important than that of fixed capital. Second, it considers the idle capital resulting from the output decline in education, for example, student decrease in primary school. The new generalized Leonitief dynamic inverse is deduced to obtain a positive solution on education when output declines as well as expands. After compiling the 2000 I-H-O table on education, the authors adopt modifications-by-step method to treat nonlinear coefficients, and calculate education scale, the requirement of human capital, and education expenditure from 2005 to 2020. It is found that structural imbalance of human capital is a serious problem for Chinese economic development. This research was supporting by the Key Project of National Natural Science Foundation of China under Grant No. 60874119, Innovation Funds of Chinese Academy of Sciences, Research Fund for Humanities and Social Sciences in colleges under Grant No. 06BJY102, Key project in Jiangxi Soft Science Project under Grant No. [2006]188, Jiangxi Research Fund for Humanities and Social Sciences in colleges under Grant No. 03JJ01, and Henan Research Fund for Humanities and Social Sciences in colleges under Grant No. 2008-ZD-002.     

Portfolio Selection with Different Borrowing-Lending Rates: Utility Maximization Model based on Mean and VaR

This article investigates a portfolio selection problem with different borrowing–lending rates and with Value-at-Risk (VaR) as the measure of risk. The problem is formulated as a utility maximization model with a general utility function that is a function of only the mean and the VaR of portfolio return. Several properties of the efficient frontier of the mean-VaR model are first obtained and then used to give some existence conditions and characterizations of the optimal solution to the utility maximization model. Further, a solution method and a numerical algorithm for solving the optimal solution are proposed. Finally, a numerical example using the real data of Chinese stock market is given to show the validity and the practicability of these results.     

Multi-label dimensionality reduction and classification with extreme learning machines

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.     

Performance comparison of distributed state estimation algorithms for power systems

A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori (MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.     

半主动空气弹簧悬挂系统控制策略及仿真分析

随着铁路不断提速，对车辆运行品质的要求也越来越高。半主动控制技术在机车车辆上的应用为进一步提高机车车辆动力学性能进而提高其运行品质开辟了新的途径。包括空气弹簧的二自由度悬挂系统的控制策略进行分析，结合控制策略提出了一套以调节空气弹簧节流孔直径从而改变悬挂系统阻尼为目的的半主动控制方案，运用计算机软件SIMULINK对半主动控制空气弹簧悬挂系统进行了仿真。研究结果表明，运用半主动控制技术可以得到更好的空气弹簧悬挂参数，提高整个悬挂系统的减振性能以改善车辆运行平稳性。     

F-generation law and recognition of system law

If a system is not disturbed (or invaded) by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the system and leads it into turbulence. Using function one direction S-rough sets, this article gives the concept of the F -generation law in the system, the generation model of the F -generation law and the recognition method of the system law. Function one direction singular rough sets is a new theory and method in recognizing the disturbance law existing in the system and recognizing the system law.     

基于支持度的改进证据合成方法

针对Dempster方法在合成高度冲突的证据时结果与直观结论相悖的问题,提出了一种基于支持度的改进证据合成方法。首先根据证据之间的相似性程度判断是否存在证据冲突,对于总数在3条以上且存在冲突的证据组合,应用各证据的支持矩阵,通过其最大特征值对应的特征向量导出各证据的支持度系数,由此对基本概率分配（basic probability assignation, BPA）函数进行转化,再采用Dempster方法进行合成。仿真计算结果表明,该方法能有效处理冲突证据组合,克服了一票否决问题,相对于其它几种典型改进算法具有更快的识别速度,能进一步减小识别结果的不确定性,并保留了Dempster方法满足交换律、结合律的良好特性。     

论减灾系统的远程公平化

远程公平化是偏向于远程的公平，旨在寻求系统整体的优化。在一定的约束条件下，通过域的分隔、变形，中心的定位与相应的远程运作机制，可消减不利因素对系统的干扰，扩大结构瓶颈，调优技术性弱点、难点、远点、易忽视点的指标期望与实现值，抑小扬大，隐近就远，弥补系统缺陷，或于不完备的条件下构建 完备系统，保障全局利益与远效，推进减灾管理的科学化，改善各个方面的减灾工作。     

一类G-框架及其对偶的若干性质

在一个新的Hilbert空间上定义了G-框架及G-对偶框架,并且讨论了他们的性质,改进和推广了其他学者的成果.