matlab12345

matalab实现银行家算法自己写的仅供交流(for learn from each other ，the matlab code by myself .)

先进PID控制MATLAB仿真（刘金琨）源代码PID程序,包含本书所有M文件(Advanced PID control MATLAB simulation (Hangzhou Press) PID program source code, including all M-book file)

说明：  径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)

这是本论文的仿真部分，是对MIMO系统理论的验证。本论文将分别从两种情况来对系统进行仿真：不同的信噪比下，发射天线数 为4，接收天线 逐渐增大；不同的信噪比下，接收天线 为4，发射天线数 逐渐增大。然后分别绘出他们的仿真图，并对它进行分析(This is the simulation part of this paper is to verify the theory of MIMO systems. In this paper, two cases will come from the system simulation: different signal to noise ratio, the number of transmission antennas is 4, the receiving antenna is gradually increasing different signal to noise ratio, the receiving antenna 4, gradually increasing the number of transmission antennas Great. And then mapped to their respective simulation map, and analyze it)

code about optimal placement of DG

试用Newton插值多项式计算f(1.13)的近似值(Trial of Newton interpolation approximation of polynomial computation f1.13 )

Scale Invariant Feature transform ( SIFT)-- this code implements the SIFT algorithm which extracts features invariant to Scale, Rotation and translation from any image.

动态系统建模与仿真第二，第四章，详细介绍MATLAB环境下的建模仿真。(Dynamic System Modeling and Simulation II, Chapter IV, details of the modeling and simulation environment MATLAB.)

this source is very helpfull for technology student, who is the first in programming with matlab,

MATLAB最优化算法中的梯度投影法，用于解决约束最优化问题。(MATLAB optimization algorithm gradient projection method for solving constrained optimization problems.)