Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 5 Articles
ââ?¬â??In this paper, a new method for feature extraction and recognition called based on QR decomposition\r\nweighted kernel fuzzy discriminant analysis (WKFDA/QR) is proposed to deal with nonlinear separable problem. Since\r\nQR decomposition on a small size matrix is adopted. A superiority of the proposed methods is its computational\r\nefficiency and can avoid the singularity. In the proposed method, the membership degree is incorporated into the\r\ndefinition of between-class and within-class scatter matrixes to get fuzzy between-class and within-class scatter\r\nmatrixes. Under different distances and different kernel functions, we compare WKFDA/QR, kernel discriminant\r\nanalysis (KDA) and fuzzy discriminant analysis (FDA) three algorithms by means of the classification rate. In addition,\r\nwe also compare WKFDA/QR with KDA and FDA under the parameters of weighted function and kernel function.\r\nExperiments on ORL and FERET two real-world data sets are performed to test and evaluate the effectiveness of the\r\nproposed algorithms and the effect of weights on classification accuracy. The results show that the effect of weighted\r\nschemes is very significantly...
The main objective of this work is to evaluate the performance of RRE in reducing the discretization error when associated with ten types of CFD numerical schemes of first, second and third orders of accuracy. The onedimensional advection-diffusion equation is solved with the finite volume method, for five values of the Peclet number (Pe), with uniform grids of 5 to 23,914,845 volumes, allowing for up to 14 RRE. Results are obtained for temperature at the center of the domain, average of the temperature field, and heat transfer rate. It was found that: (1) RRE is extremely effective in reducing the discretization error for all the variables, numerical schemes and Pe, reaching an order of accuracy of up to 18.9; and (2) The second-order central difference scheme together with RRE is the one that presents the smallest error for the dependent variable....
We investigate the solution of large linear systems of saddle point type with singular (1, 1) block by preconditioned iterative\r\nmethods and consider two parameterized block triangular preconditioners used with Krylov subspace methods which have the\r\nattractive property of improved eigenvalue clustering with increased ill-conditioning of the (1, 1) block of the saddle point matrix,\r\nincluding the choice of the parameter. Meanwhile, we analyze the spectral characteristics of two preconditioners and give the\r\noptimal parameter in practice. Numerical experiments that validate the analysis are presented....
This paper deals with nonlinear integro-differential equations that are related to the logistic equation. We\r\nwill discuss some interesting integro-differential equations which arise in applications areas such as biological or\r\necological sciences. Numerical experiments were given for nonlinear integro-differential equations using various\r\nnumerical techniques....
The particle swarm optimization (PSO) is a population-based optimization method inspired by flocking behavior \r\nof birds and human social interactions. So far, numerous modifications of PSO algorithm have been published, \r\nwhich make the PSO method more complex. Several improved PSO versions succeed in keeping the diversity of the \r\nparticles during the searching process, but at the expense of convergence speed. This paper is aimed at increasing \r\nthe rate of convergence and diversity of solutions in the population via two easy techniques: \r\n(a) Applying improved acceleration coefficients \r\n(b) Dividing search space into blocks. In particular, the second technique is efficient in the case of functions \r\nwith optimal design variables situated in the one block. Hence, instead of proposing more complex variant of PSO, \r\na simplified novel technique, called Partitioned Particle Swarm Optimizer (PPSO), has been proposed. In order to \r\nfind optimal coefficients of this method, an extensive set of experiments were conducted. Experimental results and \r\nanalysis demonstrate that PPSO outperforms nine well-known particle swarm optimization algorithms with regard \r\nto global search....
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