funkun_v36

基于MIT律的MRAC,根据MIT自适应律编写的模型参考自适应程序，适用于初学者对自适应控制的理解与应用(MIT-based law MRAC)

pattren matching the DP algorithm

改进c灰色预测方法的matlab程序，一维时间序列数据可直接使用(Improved gray prediction method c matlab program, one-dimensional time-series data can be used directly)

使用多目标优化算法解决多目标流水车间调度multi objective flowshop问题，其中main为主函数。(Using multi-objective optimization algorithms to solve multi-objective flow shop scheduling multi objective flowshop problem, where the main function main.)

凸优化程序，包络一些常用的一维搜索方法，二维优化方法，非常适用于初学者学习(Convex optimization program, envelope some common one-dimensional search methods, two-dimensional optimization method, very suitable for beginners to learn)

matlab常用函数的集合，可以作为参考书，也可以作为初学者的入门教程(great for news)

OPTIMISATION OF SIGNAL BY PARTICULE SWARM METHOD

各种使用simulink纯滞后系统仿真实例(Various simulation simulink delay system)

利用因果iir维纳滤波器对信号进行滤波，优化噪声的影响 (Iir using causal Wiener filter for filtering the signal to optimize the effects of noise )

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcomings of the steepest descent method and avoids the need for the Newton method to store And the calculation of Hesse matrix and the inverse of the shortcomings, conjugate gradient method is not only to solve large-scale linear equations one of the most useful methods, but also solution for large-scale nonlinear optimization of one of the most effective algorithm. In a variety of optimization algorithms, conjugate gradient method is a very important one. The advantage is that the required storage capacity is small, with step convergence, high stability, and does not require any external parameters.)