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matlab基础学习资料，包括例题，答案，讲解，符号运算，图像，矩正等(matlab-based learning information, including sample questions, answers, explanations, operator symbols, images, moments, etc. are)

产生右图所示图像f1(m,n)，其中图像大小为256×256，中间亮条为128 ×32，暗处=0，亮处=100。对其进行FFT： ① 同屏显示原图f1(m,n)和FFT(f1)的幅度谱图； ② 若令f2(m,n)=（-1）m+n f1(m,n)，重复以上过程，比较二者幅度 谱的异同，简述理由； ③ 若将f2(m,n)顺时针旋转90 度得到f3(m,n)，试显示FFT(f3)的幅 度谱，并与FFT(f2)的幅度谱进行比较； ④ 若将f1(m,n) 顺时针旋转90 度得到f4(m,n)，令f5(m,n)=f1(m,n)+f4(m,n)，试显 示FFT(f5)的幅度谱，并指出其与FFT(f1)和FFT(f4)的关系； ⑤ 若令f6(m,n)=f2(m,n)+f3(m,n)，试显示FFT(f6)的幅度谱，并指出其与FFT(f2)和 FFT(f3)的关系，比较FFT(f6)和FFT(f5)的幅度谱。(Generating an image f1 (m, n) shown in the figure, wherein the image size is 256 256, the intermediate light bar 128 32 0 = dark, bright Department 100. Its FFT: ① screen display picture f1 (m, n) and the FFT (f1) of the amplitude spectrum ② If so f2 (m, n) = (-1) m+n f1 (m, n), repeat The above process, comparing the amplitude spectrum of the similarities and differences between the two, brief reasons ③ If f2 (m, n) 90 degrees clockwise to get f3 (m, n), try to display FFT (f3) the amplitude spectrum and with the FFT (f2) comparing the amplitude spectrum ④ If f1 (m, n) obtained by 90 degrees clockwise f4 (m, n), so f5 (m, n) = f1 (m, n)+f4 (m, n ), try to display FFT (f5) amplitude spectrum, and pointed out its relationship with the FFT (f1) and FFT (f4) of ⑤ If so f6 (m, n) = f2 (m, n)+f3 (m, n) and try to display the FFT (f6) amplitude spectrum, and pointed out its relationship with the FFT (f2) and FFT (f3), comparing FFT (f6) and FFT (f5) amplitude spectrum.)

说明：  step response of control system

Matlab编程入门。数学建模的上手入门讲义，来自于南京大学(Introduction to Matlab programming. Introduction to Math Modeling Lecture handcuffed, from the Nanjing University)

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说明：  Welch法功率谱密度 Psd自功率谱 是用改进的平均周期图法来求随机信号的自功率谱估计 采用信号重叠分段，加窗函数和fft算法等计算一个信号的自功率谱估计（PSD）

稀疏表示分类算法在ORL人脸库上的实验，参考文章： Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31(2): 210-227. (Sparse representation classification algorithms on ORL face experiments, refer to the article: Wright J, Yang AY, Ganesh A, et al Robust face recognition via sparse representation [J] Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31 (2):.. 210-227.)

粒子群算法（PSO）优化BP神经网络，用来进行非线性的校正，效果良好(Particle swarm optimization (PSO) to optimize BP neural network, used for non-linear correction, to good effect)

书中通过大量实例，介绍了如何利用遗传算法工具箱函数编写matlab程序，解决实际问题(Through a large number of examples in the book, introduces how to use genetic algorithm toolbox function. Write the matlab program to solve the actual problem )

利用最小距离分类器分类３种鸢尾属植物 load fisheriris 载入Matlab自带的鸢尾属植物数据集 每类的前40个样本用于生成代表该类的模板，后10个作为独立的测试样本(Plant data sets The first 40 of each class used to generate the samples representative of the class template 10 after the test sample as separate)