teste_com_2

说明：  GA算法，在MATLAB上的实现，是源程序(GA algorithm, the realization in MATLAB is the source)

此代码为GUI程序设计中的uimenu的所有例子，可以让初学者迅速的掌握uimenu的使用方法。(This code for the GUI program design uimenu in all the examples, so that beginners can quickly master the use of uimenu.)

Two-Stage Least Squares method for instrumental variables estimation

声纳技术实验常用水声信号及其处理方法，包括CW信号产生，LMF信号产生(Sonar technology experiments commonly used in underwater acoustic signal processing methods, including the CW signal generator, Model LMF signal generator)

PCA/LDA/LPP/TensorLPP/代码。 LPP是目前一种比较重要的子空间算法。基于Tensor的子空间算法，是传统PCA/LDA算法的进一步推广，具有重要意义。(PCA/LDA/LO/TensorLPP/code. Alignment is a more important subspace algorithm. Based on the Tensor subspace algorithm, is a traditional PCA/LDA further promote algorithm is of great significance.)

说明：  Fuzzy C-means algorithm for clustering a data set

dc motor nominal simulink

用s函数编写光伏最大功率追踪，采用变步长电导增量法，能够实现最大功率跟踪(S function written in photovoltaic maximum power tracking, using variable step incremental conductance method to achieve maximum power point tracking)

假设一个接收到的信号为：x(t)=s(t)+n(t), 其中s(t)=A*cos(wt+a), 已知信号的频率w=1KHz， 而信号的幅度和相位未知，n(t)是一个服从N(0,1)分布的白噪声。为了利用计算机对信号进行处理， 将信号按10KHz的频率进行采样。 通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t). 在未知信号频率的情况下，通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t). (Assuming a received signal: x (t) = s (t)+ n (t), one of s (t) = A* cos (wt+ a), the known frequency signal w = 1KHz, And the signal amplitude and phase of the unknown, n (t) is a subject to N (0,1) the distribution of white noise. The use of computers in order to deal with the signal, The signal by sampling frequency of 10KHz. Of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t). Frequency signals in unknown circumstances, the adoption of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t).)

运用MATLAB编程实现遗传算法，数值仿真验证该方法的有效性，表明它能够对函数进行全局寻优。这种实现方法既可以熟悉MATLAB语言，又可以加深对遗传算法的认识和理解，以此来设计智能系统(Programming using MATLAB genetic algorithm, numerical simulation to verify the effectiveness of the method, that it is able to function for global optimization. This method can be familiar with the MATLAB language, but also better understanding of the genetic algorithm can be awareness and understanding in order to design intelligent systems)