A unified approach to fuzzy modelling and robust synchronization of different hyperchaotic systems

In this paper, a Takagi Sugeno （T-S） fuzzy model-based method is proposed to deal with the problem of synchronization of two identical or different hyperchaotic systems. The T S fuzzy models with a small number of fuzzy IF-THEN rules are employed to represent many typical hyperchaotic systems exactly. The benefit of employing the T-S fuzzy models lies in mathematical simplicity of analysis. Based on the T-S fuzzy hyperchaotic models, two fuzzy controllers arc designed via parallel distributed compensation （PDC） and exact linearization （EL） techniques to synchronize two identical hyperchaotic systems with uncertain parameters and two different hyperchaotic systems, respectively. The sufficient conditions for the robust synchronization of two identical hyperchaotic systems with uncertain parameters and the asymptotic synchronization of two different hyperchaotic systems are derived by applying the Lyapunov stability theory. This method is a universal one of synchronizing two identical or different hyperchaotic systems. Numerical examples are given to demonstrate the validity of the proposed fuzzy model and hyperchaotic synchronization scheme.     

Predictor-Corrector Smoothing Methods for Monotone LCP

In this paper, we analyze the global and local convergence properties of two predictor-corrector smoothing methods, which are based on the framework of the method in [1], for monotone linear complementarity problems (LCPs). The difference between the algorithm in [1] and our algorithms is that the neighborhood of smoothing central path in our paper is different to that in [1]. In addition, the difference between Algorithm 2.1 and the algorithm in [1] exists in the calculation of the predictor step. Comparing with the results in [1],the global and local convergence of the two methods can be obtained under very mild conditions. The global convergence of the two methods do not need the boundness of the inverse of the Jacobian. The superlinear convergence of Algorithm 2.1‘ is obtained under the assumption of nonsingularity of generalized Jacobian of Φ(x,y) at the limit point and Algorithm 2.1 obtains superlinear convergence under the assumption of strict complementarity at the solution. The efficiency of the two methods is tested by numerical experiments.     

First-principles study on the electronic structure and optical properties of T12Cd2（SO4）3 and Rb2Cd2（SO4）3

With the help of ab initio full-potential linearized augmented plane wave （FPLAPW） method, calculating the electronic structure and linear optical properties is carried out for XCd2（SO4）3 （X =Tl, Rb）. The results show that Tl2Cd2（SO4）3 （TlCdS） has a larger band gap than Rb2Cd2（SO4）3 （RbCdS） and the energy bands for RbCdS are more dispersive than those of TlCdS. From their partial densities of states （PDOS）, we have observed that the hybridization between S ionic 2p and O atomic 2p orbitals forms SO4 ionic groups. The remarkable difference between RbCdS and TlCdS is, however, the degree of hybridization between cation （Tl and Rb） and its surrounding oxygen atoms. In the view of quantum chemistry, the strong p-d hybridization indicates the existence of their cation ionic bonds （Cd-O, Rb-O, and Tl-O）. The calculations of TlCdS and RbCdS show their optical properties to be less anisotropic. Their anisotropies in the optical properties mainly occur in a low photon energy region of 5-16 eV.     

Linearization of Systems of Nonlinear Diffusion Equations

We investigate the linearization of systems of n-component nonlinear diffusion equations; such systems have physical applications in soil science, mathematical biology and invariant curve flows. Equivalence transformations of their auxiliary systems are used to identify the systems that can be linearized. We also provide several examples of systems with two-component equations, and show how to linearize them by nonlocal mappings.     

ERM learning with unbounded sampling

The learning approach of empirical risk minimization (ERM) is taken for the regression problem in the least square framework. A standard assumption for the error analysis in the literature is the uniform boundedness of the output sampling process. In this paper we abandon this boundedness assumption and conduct error analysis for the ERM learning algorithm with unbounded sampling processes satisfying an increment condition for the moments of the output. The key novelty of our analysis is a covering number argument for estimating the sample error.     

基于神经网络自适应控制线性功率放大器的设计

基于人造神经网络提出一种算法,该算法的一般特征可绘制功率放大器响应多项式的最佳系数,作为放大器的预失真器输入信号,描述了整个控制过程每一步的初始值的设定和功能。利用该方法通过对一个实际的装有对等预失真电路的功率放大器测试,测试结果表明这种方法的可行性,很适合这类功率放大器的设计。     

NONLINEAR EVOLUTION SYSTEMS AND GREEN’S FUNCTION

In this paper, we will introduce how to apply Green’s function method to get the pointwise estimates for the solutions of Cauchy problem of nonlinear evolution equations with dissipative structure. First of all, we introduce the pointwise estimates of the time-asymptotic shape of the solutions of the isentropic Navier-Stokes equations and show to exhibit the generalized Huygen’s principle. Then, for other nonlinear dissipative evolution equations, we will only introduce the result and give some brief explanations. Our approach is based on the detailed analysis of the Green’s function of the linearized system and micro-local analysis, such as frequency decomposition and so on.     

Superlinearly convergent affine scaling interior trust-region method for linear constrained LC<Superscript>1</Superscript> minimization

We extend the classical affine scaling interior trust region algorithm for the linear constrained smooth minimization problem to the nonsmooth case where the gradient of objective function is only locally Lipschitzian. We propose and analyze a new affine scaling trust-region method in association with nonmonotonic interior backtracking line search technique for solving the linear constrained LC1 optimization where the second-order derivative of the objective function is explicitly required to be locally Lipschitzian. The general trust region subproblem in the proposed algorithm is defined by minimizing an augmented affine scaling quadratic model which requires both first and second order information of the objective function subject only to an affine scaling ellipsoidal constraint in a null subspace of the augmented equality constraints. The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions where twice smoothness of the objective function is not required. Applications of the algorithm to some nonsmooth optimization problems are discussed.     

带粘性线性化BCL方程组的衰减估计

考虑三维空间R3中水波的自由表面的运动,这里研究的方程组首先是由J.L.Bona,T.Colin 和D.Lannes提出的BCL方程组.给出了带粘性的BCL方程组的线性化方程组解的长时间衰减估计,它们将在讨论非线性的带牯性的BCL方程组的整体适定性中起着关键作用.     

加性信号调制下指数形式关联噪声驱动的单模激光的光强关联时间

采用线性化近似,计算了加性信号调制下的由具有指数关联的两白噪声驱动的单模激光增益模型的光强关联时间.发现两噪声间关联程度对光强关联时间随噪声强度的变化曲线有很大的影响,两噪声间关联程度取不同值时,光强关联时间随噪声强度的变化曲线中将出现极大值(即出现共振) 或极小值(即出现抑制) .