Bernstein-type operators which preserve polynomials

In this paper we present the sequence of linear Bernstein-type operators defined for f∈C[0,1] by B_n(f°τ⁻¹)°τ, B_n being the classical Bernstein operators and τ being any function that is continuously differentiable ∞ times on [0,1], such that τ(0)=0, τ(1)=1 and τ^′(x)>0 for x∈[0,1]. We investigate its shape preserving and convergence properties, as well as its asymptotic behavior and saturation. Moreover, these operators and others of King type are compared with each other and with B_n. We present as an interesting byproduct sequences of positive linear operators of polynomial type with nice geometric shape preserving properties, which converge to the identity, which in a certain sense improve B_n in approximating a number of increasing functions, and which, apart from the constant functions, fix suitable polynomials of a prescribed degree. The notion of convexity with respect to τ plays an important role.     

Two augmentation preconditioners for nonsymmetric and indefinite saddle point linear systems with singular (1, 1) blocks

In this paper, two preconditioners based on augmentation are introduced for the solution of large saddle point-type systems with singular (1, 1) blocks. We study the spectral characteristics of the preconditioners, show that all eigenvalues of preconditioned matrices are strongly clustered. Finally, numerical experiments are also reported for illustrating the efficiency of the presented preconditioners.     

Estimating the fundamental matrix based on least absolute deviation

The epipolar geometry is the intrinsic projective geometry between two views, and the algebraic representation of it is the fundamental matrix. Estimating the fundamental matrix requires solving an over-determined equation. Many classical approaches assume that the error values of the over-determined equation obey a Gaussian distribution. However, the performances of these approaches may decrease significantly when the noise is large and heterogeneous. This paper proposes a novel technique for estimating the fundamental matrix based on least absolute deviation (LAD), which is also known as the L₁ method. Then a linear iterative algorithm is presented. The experimental results on some indoor and outdoor scenes show that the proposed algorithm yields the accurate and robust estimates of the fundamental matrix when the noise is non-Gaussian.     

The automatic model selection and variable kernel width for RBF neural networks

The Orthogonal Least Squares (OLS) algorithm has been extensively used in basis selection for RBF networks, but it is unable to perform model selection automatically because the tolerance ρ must be specified manually. This introduces noise and it is difficult to implement in the parametric complexity of real-time system. Therefore, a generic criterion that detects the optimum number of its basis functions is proposed. In this paper, not only the Bayesian Information Criterion (BIC) method, used for fitness calculation, is incorporated into the basis function selection process of the OLS algorithm for assigning its appropriate number, but also a new method is developed to optimize the widths of the Gaussian functions in order to improve the generalization performance. The augmented algorithm is employed to the Radial Basis Function Neural Networks (RBFNN) for known and unknown noise nonlinear dynamic systems and its performance is compared with the standard OLS; experimental results show that both the efficacy of BIC for fitness calculation and the importance of proper choice of basis function widths are significant.     

Simultaneous kinetic spectrophotometric determination of sulfide and sulfite ions by using an optode and the partial least squares (PLS) regression

The characterization of an optical sensor membrane is described for simultaneous determination of sulfite and sulfide ions based on the immobilization of crystal violet on a triacetylcellulose membrane. The absorbance of the membranes decreased by increasing sulfite and sulfide concentration. The partial least squares (PLS-1) calibration model based on spectrophotometric measurement for simultaneous determination of sulfite and sulfide ions was applied. This method is based on the difference between the rate of the reaction of sulfide and sulfite with membranes in pH 7.0 buffer solution and at 25 °C. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 18 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range of 200–2000 μg mL⁻¹ (2.5–25 mmol L⁻¹) and 80–900 μg mL⁻¹ (2.5–28.125 mmol L⁻¹) for sulfite and sulfide, respectively. The sensor can readily be regenerated with water and the color is fully reversible. The sensor was successfully applied to the simultaneous determination of sulfide and sulfite in water samples.     

基于线性规划和遗传–粒子群算法的烧结配料多目标综合优化方法

针对钢铁企业二次配料工艺,本文采用将硫含量折算为可比成本,兼顾节能减排目标和配料成本,建立了二次配料多目标优化模型;提出了一种基于线性规划和遗传–粒子群算法（GA–PSO）的钢铁烧结配料优化方法.首先采用线性规划算法进行求解,若线性规划方法无法求得最优解,则采用GA–PSO算法进行搜索.该方法应用于某钢铁企业360m2生产线的＂配料优化与决策支持系统＂中,实际运行结果表明,该算法在保证烧结矿质量的前提下,能够有效地减少二氧化硫排放,降低配料成本.     

欠驱动船舶航迹Backstepping自适应模糊控制

针对欠驱动船舶直线航迹跟踪问题,提出一种Backstepping自适应模糊控制方法．在模糊逼近误差存在未知上确界的假设条件下,基于Lyapunov~论证明了闭环系统在所有信号一致最终有界意义下具有均方意义稳定性．本文提出的控制器具有设计直观和结构简洁的特点,并且对参数摄动和外界干扰都具有良好的鲁棒性．在状态变量和控制输入共同约束下的仿真实验验证了该方法的有效性．     

具有数据包丢失及转移概率部分未知的网络控制系统H∞控制

研究一类具有数据包丢失及状态转移概率部分未知的网络控制系统随机稳定性及H∞控制问题．传感器与控制器之间、控制器与执行器之间存在数据包丢失的网络控制系统被建模成具有4个子系统的跳变系统,4个子系统之间的跳变遵行Markov跳变过程,并具有部分未知的跳变概率．利用Lyapunov稳定性定理及线性矩阵不等式的求解方法得到该类系统随机稳定的充分条件,并给出了相应的∞状态反馈控制器的设计方法．数值仿真结果验证了本文方法的正确性和有效性．     

时延加权融合技术的无线传感器网络控制

本文研究了无线传感器网络控制系统的建模、稳定性与控制器设计问题.首先建立一个新的无线传感器网络控制系统模型,然后提出一种用于多个传感器数据融合的时延相关加权均值方法,并通过系统建模及分析,把无线传感器网络控制系统稳定性问题转换成为具有多个传输时延和丢包现象的多传感器单控制器网络控制系统稳定性问题.并利用线性跳变系统理论...