搜索资源列表
ADAMS34
- 这个算法是在学习“用Adams三步四阶内插公式校正”时自己遍的计算这个微分方程数值解的程序，可以修改里面的方程而用于求解其他的方程，请指正！-this is the learning algorithm "Adams steps with four bands interpolation formula correction" of their times calculated for the numerical solution of differential equati
ADAMS0304
- 这个程序主要用来是用Adams三步四步法求解微分方程的，可以修改其方程而用于其他的方程求解，请指正！-this procedure is used mainly to Adams steps four-step solution of differential equations, could modify its equation and the equation for solving other, Hi!
Matrix_Mutiply
- 用于解实数范围内的超定方程组。附带有详尽的程序说明文档。具体操作步骤可以参照说明。-for real solutions within the scope of the super-equations. With a detailed descr iption of the procedures for documentation. The specific steps could references.
On-Line_MCMC_Bayesian_Model_Selection
- This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation a
liftingscheme
- 小波提升格式的源代码，采用了标准的分裂、预测和更新三步，比经典小波算法速度提高一倍！-Lifting format of the source code, adoption of the standard split, forecasts and updates of steps, the classic wavelet algorithm than double the speed!
liftingscheme_java
- 小波提升格式的java源代码，包括标准的分裂、预测和更新三步，以及一个实例-Lifting format java source code, including the standard split, forecasts and updates of steps, and an example
EM-algorithm-of-Matlab-source
- EM算法的Matlab源码 EM算法是机器学习中一个很重要的算法，即期望最大化算法，主要包括以下两个步骤： E步骤：estimate the expected values M步骤：re-estimate parameters-EM algorithm of Matlab source EM algorithm is the machine learning a very important algorithm, which maximize algorithm, mainly
Kalman
- 非常好的卡尔曼滤波学习程序。文章中有原题，有解题步骤，有实现程序。适合卡尔曼学习过程中的人。-Very good learning process Kalman filter. The article in the original title, there are problem-solving steps to achieve the procedure. Kalman learning process for people.
monituihuo
- 用蒙特卡罗方法求解函数的最值问题。步骤清晰！-Function using the Monte Carlo method to solve the problem the most value. Steps to clear!
Han_nuota
- 汉诺塔小程序的源代码 输入盘中个数后，给出移动步骤-Tower of Hanoi applet source code after the number of input, the given mobile steps
kalmanbook
- 详细说明了卡尔曼算发的使用公式及其步骤，值得阅读-Described in detail the use of Kalman counting fat formula and its steps, it is worth reading
hanoi
- 一个关于汉诺仪塔的算法，能够显示出具体的每个盘的移动步骤，还有经过了多少步骤的次数。-Hannuoba instrument on the tower algorithm, be able to show that each specific set of mobile steps to have the number after the number of steps.
MSLS
- MSLSⅠ多步递推最小二乘法 Msls分三步对系统和噪声模型进行辨识，采用脉冲序列作为辅助系统模型，用计算输出数据；用原输出数据计算,用递推最小二乘方法分别对系统参数和模型参数进行估计。 -MSLS Ⅰ recursive least squares multi-step Msls three steps on the system and noise model identification, the use of pulse sequence as a supplementary s
Hamoi
- 采用递归思想解决汉诺塔问题：详细的步骤分析，算法分析和实现。采用C++语言，可加深对递归函数的理解和应用。-Using the recursive thinking to solve Towers of Hanoi problem: detailed steps analysis, algorithm analysis and implementation. Using C++ language, can deepen the understanding and application of r
pr2050D
- 输入小孩总数n，从第k个小孩开始，循环的步长m。输出：最先输出的是第一个胜利者，其次为第二个，最后为第N个胜利者。-Enter the total number of children n, from the first k-child start the cycle of steps m. Output: The first output is the first winner, followed by the second, and finally for the first N winne
gibbs.met_1.1-3.tar
- 马尔可夫链蒙特卡洛算法，由R语言实现，是在Gibbs采样中每步利用Metropolis采样。程序非常清晰，是理解MCMC的好东西-Naive Gibbs Sampling with Metropolis Steps
LFSR
- 线性移位寄存器的源代码，在vc++6.0测试通过，可以当作集成电路BIST测试的计算机模拟仿真-LFSR source code, including the various steps of algorithm, suited to study LFSR of Friend
zuixiaoerchengfa
- 此程序时数值分析中的最小二乘法实验，步骤详细。-Numerical analysis of this program in the least-squares method experiment, the steps in detail.
Matlabcodes-RobustPCA
- Matlab codes for Robust PCA multivariate control chart-Robust PCA multivariate control chart mainly consists two steps: Step1 Calculates the robust mean and the robust covariance of original dataset using the minimum covariance determinant (M
Maths_expr215140542009
- This a simple demo of how to evaluate mathematical expressions in text format, including provision for variables and functions. The code consists of three simple classes: 1) Calc - which does the main calculations, 2) Stack - which is used t