MATLAB-SIG

Three dimensional hydrogen atom spherical harmonics visualization.

产生几种典型的电能质量扰动信号源及其该信号源的ST变换(Produced several typical power quality disturbance source and the transformation of the source ST)

说明：  柯恩类时频分布演示例程，实现信号的二次型时频分布(Cohen Class Time-Frequency Distribution demo routines to achieve signal-quadratic time-frequency distribution)

Speach Recognition with ANN and PSO

使用matlab 作图的初级教程 基础的课件 简单实用(Using MATLAB mapping)

通过虚拟阵元进行DOA估计，基于分段非线性权重值的Pso算法，基于SVPWM的三电平逆变的matlab仿真，这个有中文注释，看得明白，计算多重分形非趋势波动分析，现代信号处理中谱估计在matlab中的使用。( Conducted through virtual array DOA estimation, Based on piecewise nonlinear weight value Pso algorithm, Based on SVPWM three-level inverter matlab simulation, The Chinese have a comment, understand it, Calculate the multifractal trend fluctuation analysis, Modern signal processing used in the spectral estimation in matlab.)

能模拟物理光学实验，简单方便，不用去实验室就可以得到想要的结果！(this program simulate the physical optics experiment, simple and convenient, need not go to the lab can get the desired results!)

a new way to browse the Harvard Library collection. This is a prototype. We’re eager to hear you about what works

数学建模：亚运会公交问题，程序中编写了乘坐公交到不同站点的路线(Mathematical modeling: Asian transit issues, the program prepared to take the bus route at different sites)

(1)针对在线计算量大这一缺陷，将预测控制中的柔化输出信号的思想推广到柔化输入信号，使得约束条件被简化为仅对当前控制量的约束，可以直接计算得出；同时该方法避免了求逆矩阵，大大减小了计算量，并能够保证控制算法的可行性和良好的控制性能。 (2)针对传统算法中设计参数整定困难这一缺点，应用基于BP神经网络变参数设计的广义预测控制算法，实现了对控制量柔化参数的在线调整。 (3)利用带有遗忘因子的最小二乘法对系统辨识。本文通过仿真发现该方法对于Hénon混沌系统并不完全适用，可考虑利用其他优化系统辨识的方法对本方法进行改进，以期达到更好的辨识效果。 (4)针对系统稳定性分析复杂，本文在控制增量前加入前馈因子，保证所选的Lyapunov函数使闭环系统满足Lyapunov稳定判据，由此证明闭环系统稳定。 (1. To solve the problem of GPC huge computation, algorithm with input increment constraints is presented in which the concept of output softness was used to soften the input increments．As a result, the constraints are simplified to be the only one constraint on the current control increment which can be computed directly. At the same time, it needn’t computing the inverse matrix and thus reduces large computation. Moreover, it guarantees the feasibility of the algorithm and has good control performance． 2. To overcome the difficulty in the choice of tuning parameters in traditional GPC, a GPC algorithm with variable parameter design based on BP neural network. is presented，in which the input softness parameters are tuned on line. 3. In this paper, we Identify system by using the least square method with forgetting factor. However, after system simulation, we realize that this method doesn’t fit the Hénon chaotic system perfectly. So we recommend modify this method by other Optimizati)