100examplesofmatlab

孙志忠编写的《偏微分方程数值解法》一书中例题1.1.3的程序。用紧差分格式解常微分方程边值问题。(SUN Zhi-zhong prepared, )

Mastering MATLAB Toolbox Reference version 6.0 是英文的，不错的，希望对大家有帮助(Mastering MATLAB Toolbox Reference version 6.0 is English, yes, I hope all of you help)

基础的PSO算法程序，可以方便的解决优化问题，也可以在此基础上方便的进行改进(Based on PSO algorithm program, you can easily solve the optimization problem, on this basis can also be easily be improved)

目前公认最好的神经网络工具箱，学习性能非常好，是机器学习及人工智能，自动控制，拟合，回归预测的好工具(Currently accepted best neural network toolbox, learning performance was very good machine learning and artificial intelligence, automatic control, fit, return a good tool for forecasting)

信号检测理论——线性调频信号的匹配滤波处理(Signal detection theory- matched filtering LFM)

Adaptive tracking system is to track the video frames

实现小波包能量分解（6层分解），且易于参考从而编写出小波能量分解。(Implementation of wavelet packet energy decomposition (decomposition of 6-layer), and easy-to-reference to the preparation of a wavelet decomposition of energy.)

样本熵是时间序列复杂度的一种度量, 在上个世纪末期由几位非线 性动力学研究者提出。样本熵比近似熵更具有相对一致性(Sample entropy is a measure of the complexity of the time series, at the end of the last century by several non-linear dynamics researchers. Sample entropy more than approximate entropy relative consistency)

In this project, the standard particles swarm optimization - named here (MyPSO) is simulated and tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). Then, a new mechanism- named here (Improved MyPSO) is introduced to improve the performance of the standard particles swarm optimization to achieve better results and avoid stagnation at bad results. This new mechanism is also tested by the same benchmark functions. The results of improved PSO are then compared with those of standard PSO and different published improved PSO (SPSO, PSO-XD, CPSO-S and PSO-P5).

一种流形学习算法（很好用），用于特征降维，特征融合，相关分析等,分析了该信号的时域、频域、倒谱，循环谱等。( A fluid manifold learning algorithm (good use), For feature reduction, feature fusion, correlation analysis, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc.)