upec1

说明：  混沌系统lypunov指数谱，rucklidge系统指数(Chaotic system lypunov index spectrum, rucklidge system index)

本程序是以北京西洋参路口数据为例，采集一天24H数据，并对异常交通数据做识别、修复处理，用结合减法聚类和模糊聚类算法对路口数据实现自动划分(This procedure is based on data of Beijing Ginseng junctions, 24H data collection day, and do identify abnormal traffic data, repair treatment, with a combination of subtractive clustering and fuzzy clustering algorithm for automatic classification of data on the intersection)

利用小波分解与重构,实现图像去噪和压缩,可灵活选用其他小波,简单易懂,特别适合小波初学者.(using wavelet decomposition and reconstruction, Image Denoising and Compression, the flexibility to choose other wavelet is simple to understand and Wavelet particularly suitable for beginners.)

假设给定一个加权连通图G，G的边集合为E，顶点个数为n，要求其一棵最小生成树T。(Suppose given a weighted connected graph G, G the edge set is E, the number of vertices is n, requiring a minimum spanning tree T.)

由于matlab高低版本的不同，M文件的格式有的时候是.M有的时候是.m 这时候可以批次的修改为需要的的大写或者小写。(The capital letters exchange in matlab)

8psk modulation psk simulation

关于短路计算的一些源程序同时是有关matlab优化的源程序(Short-circuit calculations on some of the matlab source code is also optimized source)

说明：  用于求解单目标优化问题的正弦余弦算法。 SCA创建多个初始随机候选解决方案，并要求它们使用基于正弦和余弦函数的数学模型向外波动或朝向最佳解决方案。几个随机和自适应变量也被集成到该算法中，以强调在不同的优化里程碑中探索和利用搜索空间。 压缩包包括：发表的论文、优化算法代码、优化工具箱。(Sine-cosine algorithm for solving single objective optimization problems. SCA creates multiple initial random candidate solutions and requires them to use mathematical models based on sinusoidal and cosine functions to fluctuate outward or towards the best solution. Several random and adaptive variables are also integrated into the algorithm to emphasize exploring and utilizing search space in different optimization milestones. Compression packages include: published papers, optimization algorithm code, optimization toolbox.)

采用拉格朗日算法求解机组组合问题，采用的是5机系统(Lagrangian algorithm for solving unit commitment problem, machine system used is 5)

是一种时频分析方法，是对EMD方法的一种改进，具有很好的效果(Is a time-frequency analysis method, is to the EMD method is a kind of improvement, has the very good effect )