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本文利用MATLAB的遗传算法工具箱，对弦长和扭角沿风力机展向分布进行优化，得到设计工况下的最优叶片设计。目标函数是设计工况下基于动量叶素理论和Glauert等修正所得出的功率系数。在设计工况下得到的设计结果，其功率系数在一定的风速范围内都能保持一个较高的数值，同时其有效攻角分布大致都在所选翼型的升力与阻力系数的比值最大处，与理论相符合。其结果说明该优化实际的有效性。(In this paper, the use of MATLAB genetic algorithm toolbox, chord length and twist angle along the wind turbine development to distribution optimized to obtain the optimal design conditions blade design. The objective function is to design conditions based on the results of momentum blade element theory and Glauert correction power factor. Design results in the design conditions, the power factor within the range of wind speeds can maintain a higher value, the effective angle of attack distribution substantially in the the selected airfoil lift and drag coefficient ratio of maximum , theoretical analysis and show. The results indicate the validity of the actual optimization.)

An interactive peak fitting program for time-series signals, written as a self-contained function in a single m-file.

说明：  这是我自己编写的估计AR模型参数的M代码，利用的是LD算法，里面有注释，便于理解，值得推荐！(Calculates adaptive autoregressive )

感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)

例4：在sinc(t)信号中叠加噪声，分析对应频谱 fs=100 采样频率,必须大于两倍基带信号最高频率 ts=1/fs 采样时间间隔 T=2 时间窗大小 ( x=x+0.1*randn(1,N) x=awgn(x,-10, measured ) figure plot(t,x) title( 时域信号图 ) xlabel( t /s ) y=fft(x,N) figure if mod(N,2)~=0 N=N-1 end f=linspace(0,fs/2,N/2) plot(f,abs(y(1:N/2))*2/N) title( 叠加噪声的sinc(t)信号频谱 ) xlabel( f /Hz ) figure y0=y(1:N/2) P=y0.*conj(y0)/N P=10*log10(P) plot(f,P) title( 功率谱 ) xlabel( f /Hz ) )

这段程序求纵向细节v和横行细节h的合成及模极大值(maxmuim procession )

这是背包问题的程序，用matlab实现，背包问题是NP完全问题(This is the knapsack problem program, using matlab , knapsack problem is a a NP-complete problem)

用matlab设计的一个界面，能够自主添加按钮(Matlab interface design, able to independently add button)

雷达测伪距 步进频率相位差分法 matlab仿真源码(The radar measured pseudorange step frequency phase difference method matlab simulation source)

rayleigh fading channel matlab codes