PAM

max lag multiplied by 8 for raw and project representations.

GM（1,1）求解matlab代码，可计算最小二乘系数，添加了预测时间选择(GM (1,1) solve the matlab code, calculate the least squares coefficient, the timing of adding the prediction)

最新、最全“高阶谱分析工具箱”，包括全部教程和DEMO.(There is much more information in a stochastic non-Gaussian or deterministic signal than is conveyed by its autocorrelation and power spectrum. Higher-order spectra which are defined in terms of the higher-order moments or cumulants of a signal, contain this additional information. The Higher-Order Spectral Analysis (HOSA) Toolbox provides comprehensive higher-order spectral analysis capabilities for signal processing applications. The toolbox is an excellent resource for the advanced researcher and the practicing engineer, as well as the novice student who wants to learn about concepts and algorithms in statistical signal processing. The HOSA Toolbox is a collection of M-files that implement a variety of advanced signal processing algorithms for the estimation of cross- and auto-cumulants (including correlations), spectra and olyspectra,bispectrum, and bicoherence, and omputation of time-frequency distributions. Based on these, algorithms for parametric and non-parametric blin)

Four versions of the simulation is available. tsim.cpp - Prints visual representation of the simulation tsimd.cpp - Prints Density versus output flow rate and average velocity tsiml.cpp - Prints Lane Change Probabiliy versus Output flow rate and Average velocity

MATLAB6.5辅助优化计算与设计(电子工业出版社)(Optimization and design of auxiliary MATLAB6.5 (Electronic Industry Press))

Gibbs sampling算法,生成Ising model的随机样本

Transfer functions between interfaces can be easily calculated by division of the total (=upward+downward) displacement response on the corresponding interfaces.

PSO-Griewank for matlab

Simple Tabu Search in Matlab

窗函数法设计一个数字带通FIR滤波器，仿真效率很高的，独立成分分析算法降低原始数据噪声。( A window function design FIR digital band-pass filter, High simulation efficiency, Independent component analysis algorithm reduces the raw data noise.)