the-license-plate-identification

动态矩阵控制方法的matlab实现，用于求解预测控制问题(an algorithm for dynamic matrix control)

交错并联PFC boost，使用plecs仿真，仿真完美，电压电流环使用typeII补偿，CCM平均电流模式，额定功率500W，可根据需要改变参数(Interleaved PFC boost, use plecs simulation perfectly, voltage and current loop using typeII compensation, CCM average current mode, rated power 500W, parameters can be changed as needed)

说明：  ＭＯＲＬＥＴ小波脊线提取程序，实现信号小波脊线提取(MORLET wavelet ridge extraction procedures to achieve wavelet ridge extraction)

I uploaded sample code for SIMPLE-algorithm and solving lid-driven cavity flow test. It has a lot of shortcomings, but I think that it will be useful for all newcomers. I intend to develop it further, but now it very slowly converges. All notes will be useful, and hope it can be discussed.

jis0208日文编码数组,使用时将高字节减去0X20,低字节减去0X20,(jis0208 Japanese encoded array, use the high byte minus 0X20, low byte minus 0X20,)

寻找最优解或最佳路径的多种matlab算法，其中包括蚁群算法，贪婪算法，哈密顿算法，floyd算法，dijkstra算法，遗传算法等(Find the optimal solution or the best path for a variety of matlab algorithms, including the ant colony algorithm, greedy algorithm, Hamiltonian algorithm, floyd algorithm, dijkstra algorithm, genetic algorithm)

数字信号的基带传输 PAM的功率谱密度 数字基带的接收 匹配滤波器 升余弦滚降系统 基带信号眼图(Digital baseband digital signal baseband transmission power spectral density of the PAM receiver matched filter raised cosine the system baseband signal eye diagram)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

标量Kalman滤波器仿真。设计一个kalman滤波器对其进行处理得到的最佳估计。(Scalar Kalman filter simulation. Design a kalman filter to process it to get the best estimate.)

The program get a spectrum with eigenvector method