In this paper mathematical techniques have been used for the solution of Blasius differential equation. The method uses optimized artificial neural networks approximation with Sequential Quadratic Programming algorith...In this paper mathematical techniques have been used for the solution of Blasius differential equation. The method uses optimized artificial neural networks approximation with Sequential Quadratic Programming algorithm and hybrid AST-INP techniques. Numerical treatment of this problem reported in the literature is based on Shooting and Finite Differences Method, while our mathematical approach is very simple. Numerical testing showed that solutions obtained by using the proposed methods are better in accuracy than those reported in literature. Statistical analysis provided the convergence of the proposed model.展开更多
本文提出了一种混合滤波器的方法,采用扩展的Sigmoid变换,使缺陷及边界的灰度值得到加强,再通过LoG(Laplacian of a Gaussian)算子进行目标轮廓检测和相似区域的分割,最后进行细化。结果表明,该方法相对传统的中值滤波阈值分割方法能取...本文提出了一种混合滤波器的方法,采用扩展的Sigmoid变换,使缺陷及边界的灰度值得到加强,再通过LoG(Laplacian of a Gaussian)算子进行目标轮廓检测和相似区域的分割,最后进行细化。结果表明,该方法相对传统的中值滤波阈值分割方法能取得较好的检测效果。展开更多
文摘In this paper mathematical techniques have been used for the solution of Blasius differential equation. The method uses optimized artificial neural networks approximation with Sequential Quadratic Programming algorithm and hybrid AST-INP techniques. Numerical treatment of this problem reported in the literature is based on Shooting and Finite Differences Method, while our mathematical approach is very simple. Numerical testing showed that solutions obtained by using the proposed methods are better in accuracy than those reported in literature. Statistical analysis provided the convergence of the proposed model.
