m_03

graphics and data analysis program in matlab

1、分别生成两种信号（正弦波、方波、三角波、锯齿波、Sinc波形等）+噪声（高斯随机噪声、均匀噪声） 要求：频率：学号*100； 幅度：学号/10 相位：学号*pi/180 点数：学号*64 2、同时在一个图形中进行显示： 要求：标注X轴、Y轴、题目、颜色、样式、线型标注等。 3、进行信号分析及结果显示（对结果也要图形标注，同2） 要求：相关分析 卷积运算 求和运算 频谱分析（最大值、测频） 4、对上述原信号及信号分析结果分别存成EXCEL文件、文本文件和二进制文件，命名为名字+班级+学号+后缀(1, respectively, to generate two signals (sine wave, square wave, triangle wave, sawtooth wave, Sinc waveform, etc.)+ noise (Gaussian random noise, uniform noise) requirements: Frequency: No.* 100 margin: Student ID/10 phase: learning* pi/180 Points: No.* 642, in a graphical display: Requirements: X-axis label, Y axis, the title, color, style, linear marked. 3, signal analysis and results display (graphical results also marked with 2) requirements: analysis convolution summation spectrum analysis (maximum frequency measurement) 4, the original signal and the signal analysis results were kept into EXCEL files, text files and binary files, named for Name the+ class++ suffix number)

说明：  matlab智能算法的源程序，包括多种粒子群，遗传、神经网络等等(Matlab intelligent algorithm source code, including a variety of particle swarm optimization, genetic, neural network, and so on)

求解二维电磁场散射 非常准确 非常快 并且可以用来仿真亚波长的的新现象(Solving two-dimensional electromagnetic scattering very accurate and very fast simulation can be used to sub-wavelength of the new phenomenon)

KNN算法的实现，k-nearest neighbors聚类算法的matlab 实现(KNN algorithm, k-nearest neighbors clustering algorithm to achieve the matlab)

it is a matlab code for non linear programming.

Testing functions with double-byte names.

matlab中解决一般线性方程组 微分方程组等各种方程类型的解决方法 入门级的好帮手(matlab help in solving various types of equations of general solutions of differential equations of linear equations, such as the entry-level)

基于牟老师的关于三层光纤的SPR反射率计算公式的图像处理部分，自己设计的，随波长变化反射率变化曲线(The formula based on Mou teacher SPR reflectance of about three fiber optic image processing section of their own design, varies with wavelength reflectance curve)

无功补偿选址优化问题，实现成本最小，网损最少(Location reactive power compensation optimization problem to minimize costs, minimal losses)