Image-Fusion-Full-code

matlab-fem-analysis-and-application tool box

仿真实现了MIMO雷达空间谱估计，应用了Capon算法和MUSIC算法，并对两种算法下的角度估计性能进行了对比仿真(Simulation to achieve the MIMO radar spatial spectrum estimation, applied the Capon algorithm and MUSIC algorithm, and the Angle estimation performance under two kinds of algorithms are compared the simulation)

《MATLAB神经网络30个案例分析》中的第28个例子，案例28 灰色神经网络的预测算法—订单需求预测。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of 28 example, 28 cases of grey neural network prediction algorithm- order demand forecasting. Hope to have certain help to everybody! )

《MATLAB科学计算宝典》一书附录的例程,全程实例解说MATLAB数值计算和数据可视化功能，提高实用经验。包括微分，积分计算，超越方程，混沌，机械振动等的模拟。(matlab science calculate， all sorts of model for science research and so on.)

使用gevp解线性矩阵不等式的程序，很完整，有注释，对于学习求解线性矩阵不等式很有帮助(Solving linear matrix inequalities using gevp procedures are complete, with annotations, for learning useful for solving linear matrix inequalities)

检验线性方程组是否可求，用换行法求解线性方程组(Inspection linear equations can be asked whether, with a line break method is used to solve the linear equations )

用于人脸识别模型的代码~~希望能有用。网络仿真也有(failed to translate)

模糊神经网络解耦Matlab程序，比较实用的源程序！(Fuzzy Neural Network Decoupling Matlab program, more practical source!)

Discrete Fourier Transform in MATLAB

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.)