Loading...

Table of Content

    25 June 2017, Volume 30 Issue 3
    Graph Theory Methods for Decomposition w.r.t. Outputs of Boolean Control Networks
    ZOU Yunlei,ZHU Jiandong
    2017, 30(3):  519-534.  DOI: 10.1007/s11424-016-5131-3
    Asbtract ( 165 )   PDF (290KB) ( 37 )  
    Related Articles | Metrics

    This paper focuses graph theory method for the problem of decomposition w.r.t. outputs for Boolean control networks (BCNs). First, by resorting to the semi-tensor product of matrices and the matrix expression of BCNs, the definition of decomposition w.r.t. outputs is introduced. Second, by referring to the graphical structure of BCNs, a necessary and sufficient condition for the decomposition w.r.t. outputs is obtained based on graph theory method. Third, an effective algorithm to realize the maximum decomposition w.r.t. outputs is proposed. Finally, some examples are addressed to validate the theoretical results.

    An Adaptive Sliding Mode Control of Delta Operator Systems with Input Nonlinearity Containing Unknown Slope Parameters
    LIU Leipo m, FU Zhumu,SONG Xiaona
    2017, 30(3):  535-549.  DOI: 10.1007/s11424-016-5181-6
    Asbtract ( 133 )   PDF (311KB) ( 58 )  
    Related Articles | Metrics

    The problem of observer-based adaptive sliding mode control of delta operator systems with time-varying delays subject to input nonlinearity is investigated. The slope parameters of this nonlinearity are unmeasured. A novel adaptive control law is established such that the sliding surface in the state-estimation space can be reached in a finite time. A delay-dependent sufficient condition for the asymptotic stability of both the error system and the sliding mode dynamics is derived via linear matrix inequality (LMI). Finally, a simulation example is presented to show the validity and advantage of the proposed method.

    Soft-Control for Collective Opinion of Weighted DeGroot Model
    HAN Huawei,QIANG Chengcang,WANG Caiyun,HAN Jing
    2017, 30(3):  550-567.  DOI: 10.1007/s11424-017-5186-9
    Asbtract ( 140 )   PDF (695KB) ( 141 )  
    Related Articles | Metrics

    The DeGroot model is a classic model to study consensus of opinion in a group of individuals (agents). Consensus can be achieved under some circumstances. But when the group reach consensus with a convergent opinion value which is not what we expect, how can we intervene the system and change the convergent value? In this paper a mechanism named soft control is first introduced in opinion dynamics to guide the group’s opinion when the population are given and evolution rules are not allowed to change. According to the idea of soft control, one or several special agents, called shills, are added and connected to one or several normal agents in the original group. Shills act and are treated as normal agents. The authors prove that the change of convergent opinion value is decided by the initial opinion and influential value of the shill, as well as how the shill connects to normal agents. An interesting and counterintuitive phenomenon is discovered: Adding a shill with an initial opinion value which is smaller (or larger) than the original convergent opinion value dose not necessarily decrease (or increase) the convergent opinion value under some conditions. These conditions are given through mathematical analysis and they are verified by the numerical tests. The authors also find out that the convergence speed of the system varies when a shill is connected to different normal agents. Our simulations show that it is positively related to the degree of the connected normal agent in scale-free networks.

    Solution to Stochastic LQR Problem with Multiple Inputs
    XU Juanjuan,LI Lin,ZHANG Huanshui
    2017, 30(3):  568-578.  DOI: 10.1007/s11424-017-5218-5
    Asbtract ( 115 )   PDF (190KB) ( 53 )  
    Related Articles | Metrics

    This paper considers the stochastic linear quadratic regulation (LQR) problem for Itˆo stochastic systems with multiple input controllers. The explicit controllers are given in terms of two Riccati equations by introducing one new costate and establishing the homogeneous relationship between the state and the new costate. More importantly, it is more computation saving for the derived Riccati equations than the one derived by augmentation technique.

    Finite-Time Neural Funnel Control for Motor Servo Systems with Unknown Input Constraint
    CHEN Qiang,TANG Xiaoqing,NAN Yurong,REN Xuemei
    2017, 30(3):  579-594.  DOI: 10.1007/s11424-017-6028-5
    Asbtract ( 139 )   PDF (571KB) ( 126 )  
    Related Articles | Metrics

    In this paper, a finite-time neural funnel control (FTNFC) scheme is proposed for motor servo systems with unknown input constraint. To deal with the non-smooth input saturation constraint problem, a smooth non-affine function of the control input signal is employed to approximate the saturation constraint, which is further transformed into an affine form according to the mean-value theorem. A fast terminal sliding mode manifold is constructed by using a novel funnel error variable to force the tracking error falling into a prescribe boundary within a finite time. Then, a simple sigmoid neural network is utilized to approximate the unknown system nonlinearity including the saturation. Different from the prescribed performance control (PPC), the proposed finite-time neural funnel control avoids using the inverse transformed function in the controller design, and could guarantee the prescribed tracking performance without knowing the saturation bounds in prior. The effectiveness and superior performance of the proposed method are verified by comparative simulation results.

    Performance Comparison of Distributed State Estimation Algorithms for Power Systems
    SUN Yibing,FU Minyue,ZHANG Huanshui
    2017, 30(3):  595-615.  DOI: 10.1007/s11424-017-6062-3
    Asbtract ( 84 )   PDF (636KB) ( 97 )  
    Related Articles | Metrics

    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.

    Stabilization of a Non-homogeneous Rotating Body-Beam System with the Torque and Nonlinear Distributed Controls
    GUO Yaping,WANG Junmin
    2017, 30(3):  616-626.  DOI: 10.1007/s11424-017-5235-4
    Asbtract ( 63 )   PDF (214KB) ( 56 )  
    Related Articles | Metrics

    This paper considers the stabilization of non-homogeneous rotating body-beam system with the torque and nonlinear distributed controls. To stabilize the system, the authors propose the torque and nonlinear distributed controls applied on the disk and flexible beam respectively. As long as the angular velocity of the disk does not exceed the square root of the first eigenvalue of the related self-adjoint positive definite operator, the authors show that the torque and nonlinear distributed control laws suppress the system vibrations, in the sense that the beam vibrations are forced to decay exponentially to zero and the body rotates with a desired angular velocity.

    The Distributed Representation for Societal Risk Classification toward BBS Posts
    CHEN Jindong,TANG Xijin
    2017, 30(3):  627-644.  DOI: 10.1007/s11424-016-5099-z
    Asbtract ( 109 )   PDF (419KB) ( 91 )  
    Related Articles | Metrics

    The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.

    Pricing Credit Derivatives Under Fractional Stochastic Interest Rate Models with Jumps
    ZHANG Jiaojiao,BI Xiuchun,LI Rong,ZHANG Shuguang
    2017, 30(3):  645-659.  DOI: 10.1007/s11424-017-5126-8
    Asbtract ( 75 )   PDF (219KB) ( 136 )  
    Related Articles | Metrics

    Based on the reduced-form approach, this paper investigates the pricing problems of default-risk bonds and credit default swaps (CDSs) for a fractional stochastic interest rate model with jump under the framework of primary-secondary. Using properties of the quasi-martingale with respect to the fractional Brownian motion and the jump technique in Park (2008), the authors first derive the explicit pricing formula of defaultable bonds. Then, based on the newly obtained pricing formula of defaultable bonds, the CDS is priced by the arbitrage-free principle. This paper presents an extension of the primary-secondary framework in Jarrow and Yu (2001).

    Copula-Based Risk Management Models for Multivariable RMB Exchange Rate in the Process of RMB Internationalization
    DU Jiangze,LAI Kin Keung
    2017, 30(3):  660-679.  DOI: 10.1007/s11424-017-5147-3
    Asbtract ( 112 )   PDF (316KB) ( 102 )  
    Related Articles | Metrics

    This paper investigates the dependence of the exchange rate of onshore Renminbi (RMB) and offshore RMB against US dollar (i.e., CNY and CNH) based on copula models. Eleven different copulas were selected to construct multivariate distribution and estimate the value-at-risk for RMB exchange rate. Empirical results show that time-invariant Student-t copula is the best model to fit the sample data. The positive upper and lower dependence indicates that CNY and CNH series tend to move in the same direction. Moreover, the dependence between the two exchange rates is asymmetric, which means that traditional models, such as Pearson’s correlation, are inappropriate to measure the correlations between these markets. The best fitted model is chosen to estimate the financial risk, which can help business practitioners and policymakers track risk evolution and make good decisions.

    Cooperation in Two-Stage Games on Undirected Networks
    GAO Hongwei,PETROSYAN Leon,QIAO Han,SEDAKOV Artem
    2017, 30(3):  680-693.  DOI: 10.1007/s11424-016-5164-7
    Asbtract ( 114 )   PDF (256KB) ( 98 )  
    Related Articles | Metrics

    In the paper, cooperative two-stage network games are studied. At the first stage of the game, players form a network, while at the second stage players choose their behaviors according to the network realized at the first stage. As a cooperative solution concept in the game, the core is considered. It is proved that some imputations from the core are time inconsistent, whereas one can design for them a time-consistent imputation distribution procedure. Moreover, the strong time consistency problem is also investigated.

    Variable Selection in Joint Location, Scale and Skewness Models of the Skew-Normal Distribution
    LI Huiqiong,WU Liucang,MA Ting
    2017, 30(3):  694-709.  DOI: 10.1007/s11424-016-5193-2
    Asbtract ( 145 )   PDF (280KB) ( 93 )  
    Related Articles | Metrics

    Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and (or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes, and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.

    The ANOVA-Type Inference in Linear Mixed Model with Skew-Normal Error
    WU Mixia,ZHAO Jing,WANG Tonghui,ZHAO Yan
    2017, 30(3):  710-720.  DOI: 10.1007/s11424-017-5253-2
    Asbtract ( 145 )   PDF (212KB) ( 52 )  
    Related Articles | Metrics

    Linear mixed effect models with skew-normal errors and distribution-free random effects are considered. The ANOVA-type F-tests are proposed to test the significance of random effects and the hypothesis on fixed effects of interest, respectively. Both tests are proved to be exact F-tests under this model, and the exact confidence interval for fixed effects of interest is derived. Simulation results are given to study the powers of ANOVA-type tests.

    Degree-Based Moment Estimation for Ordered Networks
    LI Wenlong,YAN Ting,ABD ELGAWAD Mohamed,QIN Hong
    2017, 30(3):  721-733.  DOI: 10.1007/s11424-017-5307-5
    Asbtract ( 105 )   PDF (756KB) ( 56 )  
    Related Articles | Metrics

    The edges between vertices in networks take not only the common binary values, but also the ordered values in some situations (e.g., the measurement of the relationship between people from worst to best in social networks). In this paper, the authors study the asymptotic property of the moment estimator based on the degrees of vertices in ordered networks whose edges are ordered random variables. In particular, the authors establish the uniform consistency and the asymptotic normality of the moment estimator when the number of parameters goes to infinity. Simulations and a real data example are provided to illustrate asymptotic results.

    Partially Function Linear Error-in-Response Models with Validation Data
    ZHANG Tao,MENG Jiafu,WANG Bin
    2017, 30(3):  734-750.  DOI: 10.1007/s11424-017-5263-0
    Asbtract ( 135 )   PDF (341KB) ( 45 )  
    Related Articles | Metrics

    This paper considers partial function linear models of the form Y =  X(t)β(t)dt + g(T) with Y measured with error. The authors propose an estimation procedure when the basis functions are data driven, such as with functional principal components. Estimators of β(t) and g(t) with the primary data and validation data are presented and some asymptotic results are given. Finite sample properties are investigated through some simulation study and a real data application.