bysj-program

Convolutional(2,1,6) Encoder and soft decision Viterbi Decoder

exm for Experiments with MATLAB (by Cleve Moler in 2008), 这本书是关于MATLAB的教材，乃MATLAB首席科学家兼董事长Cleve Moler的著作。(exm for Experiments with MATLAB (by Cleve Moler in 2008), this book is about teaching MATLAB, MATLAB is the chief scientist and chairman of Cleve Moler)

基于matlab的分类支持向量机，对于初学者比较有帮助。(Matlab-based classification of support vector machines, more helpful for beginners.)

混合蛙跳算法（Shuffled Frogleaping Algorithm）是进化计算领域新发展起来的一种优化算法，由Eusuff和Lansey与2003年为解决组合优化问题而最先提出(shuffled frogleaping Algorithm，)

Inverter pole is simulated

letter A draw using B-spline. implementation in matlab

实现光伏电池输出的稳压。测试效果明显，对一般精确度的模块足够实现(pvmode，this is a good wenya mode)

Automatic integration of functional differential equations: an approach, ACM TOMS, 1 (1975) 357-368.一文中的时滞微分方程求解程序。(Example 1 of K.W. Neves, Automatic integration of functional differential equations: an approach, ACM TOMS, 1 (1975) 357-368.)

灰色预测模型称为CM模型，G为grey的第一个字母，M为model的第一个字母。GM（1，1）表示一阶的，一个变量的微分方程型预测模型。GM（1，1）是一阶单序列的线性动态模型，主要用于时间序列预测。 一、GM（1，1）建模 设有数列 共有 个观察值 对 作累加生成，得到新的数列 ，其元素 （5-1） 有： 对数列 ，可建立预测模型的白化形式方程， （5-2） 式中： ——为待估计参数。分别称为发展灰数和内生控制灰数。设 为待估计参数向量 则 按最小二乘法求解， 有： （5-3） 式中： （5-4） （5-5） 将（5-3）式求得的 代入（5-2）式，并解微分方程，有 （1，1）预测模型为： （5-6）(gray forecasting model called CM model, the G-gray of a letter, the M model for the first letter. GM (1,1), a band of a variable type of differential equation models. GM (1,1) is a sequence of single-band linear dynamic model, mainly for time series prediction. A GM (1,1) model series with a total value of observation for the cumulative production, to a new series of its elements (5-1) : The series, we can establish the prediction model albino form of equation (5-2) where :-- to question the estimated parameters. The development will be known as the gray and hygiene control within a few gray. Set parameters to be estimated according to Vector least squares method, are : (5-3) where : (5-4) (5-5) (5-3) that obtained in lieu of income (5-2)- and solutions differential equations, (1 1) Fore)

K-L+LDA结合了K-L和LDA优点的一个算法(K-L+ LDA combination of KL and the advantages of an algorithm for LDA)