Lab6

说明：  遗传算法的小程序，希望能够对大家有用处！(genetic algorithm for small procedures, we hope to be able to be useful!)

说明：  单环掺铒光纤激光器光延迟反馈的混沌控制，希望有用(The superlattice optical quantum well bi-stable system of global split ends And chaos behavior)

雷达仿真程序，包括雷达系统中会用到的一些常用程序(Radar simulation program, including the radar system will be used in some commonly used procedures)

matlab实现信号的相参积累和非相参积累源代码(matlab realization the signal coherent integration and non-coherent accumulation of source code)

说明：  his work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multiobjective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems.

欧拉角演示，更直观深入理解3个欧拉角的内涵(Euler angle presentation and more intuitive 3-depth understanding of the meaning of the Euler angle)

对图像进行对比增强。没有用matlab命令 而是内核 想研究的同志可以(right image contrast enhancement. Not using Matlab order to study the kernel but the comrades can s)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

基2FFT算法，不使用FFT函数，包括信号显示、幅值谱、相位谱……(Base 2FFT algorithm does not use FFT functions, including signal display, amplitude spectrum and phase spectrum ......)

通过设计一个二阶加权系数自适应横向FIR滤波器，对一个加随机噪声的正弦信号实现滤波。 具体设计方案为： 1，生成标准正弦信号S 2，生成等长的随机信号N 3，生成加随机噪声的正弦信号X 4，X通过参数可调数字滤波器，输出Y 5，Y与参考信号作差得到误差E 6，E通过自适应算法调整权值W 7，用LMS算法处理噪声干扰的信号，最终实现滤波器功能(A two order weighted coefficient adaptive transverse FIR filter is designed to filter a sinusoidal signal with random noise. The specific design scheme is as follows: 1, generating a standard sinusoidal signal S 2, generating an equal length random signal N 3, generating a sinusoidal signal X with random noise 4, X can adjust the digital filter by parameter and output Y 5, the difference between the Y and the reference signal is E 6, E adjusts the weight W through the adaptive algorithm 7, the LMS algorithm is used to deal with the noise interference signal, and the filter function is finally realized.)