MATLAB-code

说明：  《模糊逻辑》（Fuzzy Logic）一书的例子程序，作者： Reza Langari("fuzzy logic" (Fuzzy Logic), an example of the procedure, the authors : Reza including)

单神经元PID控制器的仿真与应用实例。比较常规PID控制器，得其优缺点(single neuron PID controller simulation and application examples. Comparing Conventional PID controller, to gain advantages)

skeletal action recognition

Tomographic Inversion of pn waves. Thesis and matlab codes in the appendix.

管道内圆柱绕流程序，使用格子玻尔兹曼方法(Channel flow past a cylinderical obstacle, using a LB method)

Face recognition is still a challenging task due to the different problems such as pose, illumination, expression and occlusions. Due to these problems, it needs an extensive research. Various face recognition techniques exists but they suffer one or the other limitations.

matlab pso算法标准源代码，适用于初学群体(matlab psoAlgorithm source code)

在MATLAB环境下实现了CDMA通信系统仿真，程序流程为：随机序列产生--WALSH码扩频--BPSK调制--加入（和不加入）高斯白噪声--BPSK解调--WALSH码解扩，对过程波形进行了存储，对加噪与不加噪情况下的解码结果进行了对比，并绘制出信噪比与误码率关系图。(In the MATLAB environment to achieve a CDMA communication system simulation, the program flow as follows: random sequence generated- WALSH yards Spread Spectrum- BPSK modulation- to join (and not to join) Gaussian white noise- BPSK demodulator- WALSH yards Solution expansion of the process of waveform storage, to add noise and without noise cases, the decoding results were compared, and draw out the signal to noise ratio and bit error rate diagram.)

调用smooth函数进行加噪数据的平滑处理(noise and smooth )

运用粒子群优化算法对ARMA(p,q)模型的阶数进行优化是可行的，且利用优化后的ARMA模型对航空发电机状态趋势进行分析的结果十分准确，与原始数据所表现出的航空发电机状态变化趋势基本一致。经计算，此时分析结果的平均相对误差较小，仅为1.38 ，与优化前构建的ARMA模型相比，平均相对误差降低0.23个百分点，优化效果明显。(Using particle swarm optimization algorithm to optimize the order of the ARMA (p, q) model is feasible, and the use of the results of the ARMA model optimized air generator status trends analysis is very accurate, and the raw data showed aviation the generator status consistent trend. Calculated analysis of the results, the average relative error is small, only 1.38 , compared with the the ARMA model built before optimization, average relative error is reduced by 0.23 percentage points, to optimize the effect is obvious.)