Degree Switching Operations in Networks and Large Scale Systems Assignment Problems

In this paper an algorithm is presented for listing all output sets for a large sparse square matrix A arising in large scale systems applications using network theory and the degree switching operations. The algorithm exploits the zero nonzero structure of matrix A and uses optimum data structures and data manipulation methods. The method is shown to be useful in finding all optimum assignments in an n x n optimum assignment problem and generation of all digraphs that can be associated with an n x nsparse matrix. The problem of testing whether there exists a set of vertex disjoint cycles of specified lengths in a network is shown to be NP-complete.     

Network topology identification under the multi-agent agreement protocol

This paper investigates the problem of identifying the interaction geometry of a set of agents, whose collective goal are to achieve consensus under an agreement protocol. By classifying agents into different subsets based on their behavior, as well as introducing the so-called input and output agents, a relationship between the transfer function matrix and the identifiability of system parameters is established. Specifically, two cases are considered. If the set of input agents coincides with the set of output agents, the number of edges in the input agent set, in the complement of input agent set, and between these two sets can be uniquely identified. Thus, the search space of feasible graphs becomes much smaller. The problem can be solved in polynomial time, and an algorithm is provided. Moreover, if all the agents in the system are output agents, parameters of the system can be uniquely identified, and an algebraic method is given to exactly recover the graph topology. A numerical example illustrates the effectiveness of the proposed algorithm.     

An off-line approach for output feedback robust model predictive control

For constrained linear parameter varying systems subject to bounded disturbances and noises, this article investigates an off-line output feedback robust model predictive control approach. The sub-observer gains with robust positively invariant sets, and sub-controller gains with robust control invariant sets are simultaneously off-line optimized and stored in a look-up table. According to real-time estimation error bounds and estimated states, the time-varying sub-observer gains and sub-controller gains are on-line searched. The proposed off-line output feedback robust model predictive control approach with the guarantee of nested robust positively invariant sets and robust control invariant sets in theory reduces the on-line computational burden.     

一种基于属性核的Rough集属性约简搜索算法

本文提出了一种新的可以快速寻找Rough集约简属性的算法,该算法利用计算属性核来提高计算效率.首先,从属性核出发,不断地在属性核上添加非核属性以构成新的属性集合,并判断新的属性集合是否为属性约简,然后在此基础上进行剪枝运算并在不构成约简的属性集合的基础上再生成新的属性集合并进行属性约简判断.该算法为一种搜索算法,由于采用了属性核算法和剪枝算法,提高了计算效率并能够找到给定信息系统的全部属性约简.通过测试,表明该算法是一种可行且有效的属性约简算法.     

计算紧半代数集的同调方法

提出一个关于计算紧半代数集构成排列的0维Betti-数和1维Betti-数的算法.这个算法的复杂度为单指数复杂度.     

基于快速傅立叶变换的信号频谱分析仿真

信号序列的DFT本身就是信号频谱的采样集,所以DFT可直接用于分析信号的频谱。快速傅立叶变换是一种有效、实用的信号DFT算法。文章通过对DIT-FFT算法的详细分析,提出了一种计算机软件仿真信号频谱分析方案,并给出了关键算法的C语言程序。     

Global stabilization of high-order constrained nonlinear systems: An unbounded time-varying scaling based approach

Global stabilization of high-order nonlinear systems is studied with an asymmetric output constraint. A novel approach is raised by incorporating the unbounded time-varying scaling idea into the barrier Lyapunov function method. This is also suitable for systems with symmetric output constraints and without output constraints simultaneously. By the recursive design algorithm, a time-varying controller is established to ensure that state asymptotically converges to zero and output is always keeping in the given asymmetric domain. Finally, the feasibility of the control scheme is shown with an example.     

政府科技资助对企业R&D产出的影响——基于我国大中型工业企业的实证研究

本文基于文献研究,构建了企业R&D投入产出的计量模型,采用我国大中型工业企业的相关数据,测度了政府科技资助、企业自筹R&D资金、金融机构贷款等对企业R&D产出的影响。进一步,分别考察了政府资助强度、企业技术特征对政府科技资助与企业R&D产出绩效关系的影响。研究发现:政府科技资助对企业的R&D产出有明显的促进作用,但是作用不及企业自筹的R&D资金;政府科技资助对中等资助强度产业的R&D产出有着明显的促进作用;政府科技资助对低、中低技术产业的R&D产出有着明显的促进作用,金融机构贷款对企业R&D产出的作用不显著。     

Auxiliary model based least squares parameter estimation algorithm for feedback nonlinear systems using the hierarchical identification principle

This paper presents a decomposition based least squares estimation algorithm for a feedback nonlinear system with an output error model for the open-loop part by using the auxiliary model identification idea and the hierarchical identification principle and by decomposing a system into two subsystems. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm has a smaller computational burden. The simulation results indicate that the proposed algorithm can estimate the parameters of feedback nonlinear systems effectively.     

邻接矩阵在高校图书馆借阅数据挖掘中的应用

Apriori算法是关联规则挖掘的一个经典算法,针对Apriori算法的不足,提出了基于邻接矩阵的算法,该算法首先用邻接矩阵将事务数据库表示出来,然后基于邻接矩阵生成频繁k_项集。以高校图书馆借阅历史数据的挖掘为例,详细描述了事务数据库相应的邻接矩阵生成算法、k_项集生成算法以及相应的数据结构设计,算法均采用C语言描述。     

Broad echo state network for multivariate time series prediction

In this paper, a broad echo state network with multiple reservoirs in parallel configuration (Broad-ESN) is proposed for a class of multivariate time series prediction. Firstly, through the unsupervised learning algorithm of restricted Boltzmann machine (RBM), the number of reservoirs of Broad-ESN can be determined, such that the dynamic characteristics of a class of multivariate time series can be fully reflected. Secondly, a parameter optimization method based on Davidon–Fletcher–Powell (DFP) quasi-Newton algorithm is proposed to optimize the reservoir parameters of Broad-ESN. Meanwhile, an output weights learning method based on output error is given to train the output weights of Broad-ESN. Thirdly, a sufficient condition for the echo state property of Broad-ESN is given. Finally, four examples are given to verify the effectiveness of Broad-ESN.     

基于模糊关系的人力资源管理工作分配算法

工作分配是人力资源管理的一项重要职能,经典的工作分配算法建立在考核明确和任务性质相同的基础上,正因为如此所以限制了其实际工作中的应用。基于模糊关系的工作分配算法对经典的工作分配算法进行了扩展,通过建立工作满意程度的模糊集和员工个体能力矩阵,应用匈牙利法求解工作分配方案。     

Leader-following rendezvous for uncertain Euler–Lagrange multi-agent systems by output feedback

In this paper, we investigate the problem of output feedback tracking for a class of Euler–Lagrange multi-agent systems with unmeasurable velocity and input disturbances. By proposing a novel dynamic velocity observer, an adaptive output feedback consensus algorithm is proposed such that the tracking errors of all agents can converge to an arbitrarily small neighborhood of zero by tuning the design parameters. A numerical example is presented to illustrate the effectiveness of the controller.     

Polytopic invariant and contractive sets for closed-loop discrete fuzzy systems

In this work a procedure for obtaining polytopic λ-contractive sets for Takagi–Sugeno fuzzy systems is presented, adapting well-known algorithms from literature on discrete-time linear difference inclusions (LDI) to multi-dimensional summations. As a complexity parameter increases, these sets tend to the maximal invariant set of the system when no information on the shape of the membership functions is available. λ-contractive sets are naturally associated to level sets of polyhedral Lyapunov functions proving a decay-rate of λ. The paper proves that the proposed algorithm obtains better results than a class of Lyapunov methods for the same complexity degree: if such a Lyapunov function exists, the proposed algorithm converges in a finite number of steps and proves a larger λ-contractive set.     

Filtering search results using an optimal set of terms identified by an artificial neural network

Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm.     

Robust estimations of the Region of Attraction using invariant sets

The Region of Attraction of an equilibrium point is the set of initial conditions whose trajectories converge to it asymptotically. This article, building on a recent work on positively invariant sets, deals with inner estimates of the ROA of polynomial nonlinear dynamics. The problem is solved numerically by means of Sum Of Squares relaxations, which allow set containment conditions to be enforced. Numerical issues related to the ensuing optimization are discussed and strategies to tackle them are proposed. These range from the adoption of different iterative methods to the reduction of the polynomial variables involved in the optimization. The main contribution of the work is an algorithm to perform the ROA calculation for systems subject to modeling uncertainties, and its applicability is showcased with two case studies of increasing complexity. Results, for both nominal and uncertain systems, are compared with a standard algorithm from the literature based on Lyapunov function level sets. They confirm the advantages in adopting the invariant sets approach, and show that as the size of the system and the number of uncertainty increase, the proposed heuristics ameliorate the commented numerical issues.     

The filtering based parameter identification for bilinear-in-parameter systems

This paper discusses the parameter estimation for a class of bilinear-in-parameter systems with colored noise. By utilizing the filtering technique, we derive the relationship between the filtered output and the measurement output and obtain two linear regressive sub-models. A filtering based multi-innovation stochastic gradient algorithm is derived for interactively identifying each sub-model. The proposed algorithm avoids the estimation of correlated noise and improves the parameter estimation accuracy by making full use of the measurement data. The numerical simulation results indicate that the proposed algorithm has higher estimation accuracy than the hierarchical multi-innovation stochastic gradient algorithm.     

欧佩克产量对国际油价动态影响的实证分析

基于时国际油价和欧佩克产量的结构性突变检验,本文分三时期建立了国际油价与欧佩克产量的误差修正模型,采用脉冲响应函数和方差分解方法对比不同时期欧佩克产量对国际油价的影响力.结果表明,Ⅲ期国际油价对欧佩克产量冲击的反应期较Ⅱ期提前,国际油价预测均方误差中欧佩克产量贡献率与Ⅰ期接近.表明欧佩克产量对国际油价的短期影响力在逐渐恢复.     

Incomplete-profile potential games

The incomplete-profile normal form game (IPNFG), which contains several infeasible profiles, is considered. First, the dynamics of evolutionary IPNFG are presented. Then a method is provided to verify whether an IPNFG is potential. Certain properties of potential IPNFGs are revealed. Next, an algorithm is provided to search the feasible set to guarantee that the corresponding IPNFG is potential. In addition, the decomposition of an IPNFG is investigated. Finally, for an IPNFG with several feasible sets, an algorithm is proposed to find the one which makes the corresponding IPNFG closest to a potential game.