Iterative support vector machine with guaranteed accuracy and run time

Abstract: Using a conjugate gradient method, a novel iterative support vector machine (FISVM) is proposed, which is capable of generating a new non‐linear classifier. We attempt to solve a modified primal problem of proximal support vector machine (PSVM) and show that the solution of the modified primal problem reduces to solving just a system of linear equations as opposed to a quadratic programming problem in SVM. This algorithm not only has no requirement for special optimization solvers, such as linear or quadratic programming tools, but also guarantees fast convergence. The full algorithm merely needs four lines of MATLAB codes, which gives results that are similar to or better than that of several new learning algorithms, in terms of classification accuracy. Besides, the proposed stand‐alone approach is capable of dealing with instability of classification performance of smooth support vector machine, generalized proximal support vector machine, PSVM and reduced support vector machine. Experiments carried out on UCI datasets show the effectiveness of our approach.     

Self-scaled conjugate gradient training algorithms

This article presents some efficient training algorithms, based on conjugate gradient optimization methods. In addition to the existing conjugate gradient training algorithms, we introduce Perry's conjugate gradient method as a training algorithm [A. Perry, A modified conjugate gradient algorithm, Operations Research 26 (1978) 26–43]. Perry's method has been proven to be a very efficient method in the context of unconstrained optimization, but it has never been used in MLP training. Furthermore, a new class of conjugate gradient (CG) methods is proposed, called self-scaled CG methods, which are derived from the principles of Hestenes–Stiefel, Fletcher–Reeves, Polak–Ribière and Perry's method. This class is based on the spectral scaling parameter introduced in [J. Barzilai, J.M. Borwein, Two point step size gradient methods, IMA Journal of Numerical Analysis 8 (1988) 141–148]. The spectral scaling parameter contains second order information without estimating the Hessian matrix. Furthermore, we incorporate to the CG training algorithms an efficient line search technique based on the Wolfe conditions and on safeguarded cubic interpolation [D.F. Shanno, K.H. Phua, Minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software 2 (1976) 87–94]. In addition, the initial learning rate parameter, fed to the line search technique, was automatically adapted at each iteration by a closed formula proposed in [D.F. Shanno, K.H. Phua, Minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software 2 (1976) 87–94; D.G. Sotiropoulos, A.E. Kostopoulos, T.N. Grapsa, A spectral version of Perry's conjugate gradient method for neural network training, in: D.T. Tsahalis (Ed.), Fourth GRACM Congress on Computational Mechanics, vol. 1, 2002, pp. 172–179]. Finally, an efficient restarting procedure was employed in order to further improve the effectiveness of the CG training algorithms. Experimental results show that, in general, the new class of methods can perform better with a much lower computational cost and better success performance.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

基于CG-MSNWF的空时自适应抗干扰算法研究

针对多级维纳滤波MSNWF(Multi-Stage Nested Winner Filter)算法的计算量大,在处理高维数据时不能满足实时性的需要问题,采用基于共轭梯度CG(Conjugate Gradient)的多级维纳滤波算法—CG-MSNWF。在相同的干扰抑制性能条件下,该算法相比于MSNWF不需要后向迭代过程,降低了计算量和对硬件存储器的要求,提高了算法的收敛速度,满足了实时性的需求。仿真结果证实了算法的实效性。     

分子模拟在聚合物膜研究中的应用

简要回顾了近15年来分子模拟在各种聚合物膜研究中的应用,分析了它为宏观实验现象提供的新解释以及给聚合物膜的改性和设计研究工作带来的新思路,同时也对当前计算条件下模拟中存在的问题进行了探讨。     

聚合硫酸硅酸铁絮凝剂的制备方法及性能研究

用四种方法制备出硅铁比不同的聚合硫酸硅酸铁絮凝剂，通过对造纸废水的处理，比较了各种制备方法和各种不同硅铁比下产品的性能。结果显示在硅铁比为1：20时，直接向聚铁中加入试剂硅酸钠所得产品絮凝效果最好；向聚铁中加入由工业水玻璃酸性聚合而得的聚硅酸所得产品絮凝效果较好且价格较低。得到了制备聚合硫酸硅酸铁的最佳工艺条件。     

回波对消中盲源分离算法的研究与仿真

盲源分离因为可以仅根据接收到的观测信号来估计还原源信号,成为近年来在信号处理领域的研究热点.针对盲源分离中的基于峭度的独立分量分析算法在源信号还原的过程中计算量较大的问题,采用共轭梯度方法对独立分量分析法进行优化,优化后的算法收敛速度更快,稳态误差变小.Matlab仿真实验表明,优化后的独立分量分析算法收敛更快,实际分离效果更好.     

The removal of arsenic from （ground）-water by adsorbent loaded in polymeric microspheres

Arsenic is a natural tasteless and odourless element,existing in the earth’s crust at average levels of between two and five thousands micrograms per liter (parts per million) . Arsenic is highly toxic to humans, who are exposed to it primarily from air,food and water. The occurrence of arsenic in groundwater is due to geological composition of soil. High concentrations of arsenic in water are the result of dissolution or desorption of ferric oxides and the oxidation of mineral arsenopyrites. Arsenic in drinking water has an important impact on the human health,especially in the less developed countries. Different methods exist to remove arsenic from aquatic media,and one of them is by adsorption. In this work,the adsorption of both As(III) and As(V) by means of novel microspheres has been investigated. In particular,TiO2 has been embedded into polymeric microspheres PES (PolyEtherSulphone) and PEEK-WC (PolyEtherEther-Ketone) . The main advantages of this encapsulation adsorption material are: no loss of adsorbents into the water stream,easy to be used and scaled-up.