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200301050545025522
- 医院系统毕业设计 经过分析,我们使用 MICROSOFT公司的 vb开发工具,利用其提供的各种面向对象的开发工具,尤其是数据窗口这一能方便而简洁操纵数据库的智能化对象,首先在短时间内建立系统应用原型,然后,对初始原型系统进行需求迭代,不断修正和改进,直到形成用户满意的可行系统。 软件源程序-hospital system after graduation design analysis, we used the MICROSOFT company vb devel
reccate
- 迭代计算一个四阶黎卡提方程-iterative calculation of a four bands Riccati equation
siddon
- 在迭代算法重建过程中，利用siddon算法求解系统矩阵的过程。-In the process of iterative reconstruction algorithm, using siddon algorithm process of system matrix
GaussSeidel
- Gauss-Seidel method高斯赛德尔迭代法求解线性方程组问题matlab代码-Gauss-Seidel method
shuzhi
- 用于解线性方程组的迭代法，包括jacobi，gauss，sor，共轭等方法-For solving linear equations of iterative method, including jacobi, gauss, sor, conjugated
diedai
- 多输入多输出的迭代学习算法的一个示例，代码直接可用，里面的一些注释自己可以修改-a sample of terative learning algorithm with multiple input multiple output ，inside some comments can be modified by yourself
Rd_iteration
- 限失真函数的迭代算法,完成限失真函数的R(d)的迭代计算，并画出R(d)函数曲线-the iteration algorithm of R(d)
SOR
- 这是松弛迭代法(SOR)的源代码,对解决线性方程组求解问题很有帮组-This is a relaxation iterative method (SOR) of the source code to solve linear equations to solve the problem is to help group
cx
- 有限元源程序 有FORTAN 编写的是非线性有限元程序迭代模块演示源程序-Finite Element FORTAN source code has been prepared in nonlinear finite element procedures are iterative source module demo
Desktop105
- 主要用C++算法写数值分析几种方法几种迭代法的相互差异和源代码-Is mainly used C++ algorithm for numerical analysis of several ways to write several iterative method of mutual differences and the source code
vector
- 关于向量的知识点，讲述了向量的用法，向量的作用，还介绍了迭代器的用法-knowledge and function of vector
cx
- 有限元源程序 有FORTAN 编写的是非线性有限元程序迭代模块演示源程序-Finite Element FORTAN source code has been prepared in nonlinear finite element procedures are iterative source module demo
Desktop105
- 主要用C++算法写数值分析几种方法几种迭代法的相互差异和源代码-Is mainly used C++ algorithm for numerical analysis of several ways to write several iterative method of mutual differences and the source code
Adaboost
- Adaboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器(强分类器)。(Adaboost is an iterative algorithm, and its core idea is different for the same training set training classifier (weak classifier), then put these weak classifier together