FullBNT-beiyesi

mimo system caculate,decode,error rate (mimo system space-time code)

Z-Source变换器matlab仿真模型，输入电压24V，输出电压110V。(Z-Source converter， matlab models.)

IMM算法仿真,Matlab源码,用了最小二乘法，EKF，UFK等几种方法比较(IMM algorithm simulation, Matlab source code, using the least squares method, EKF, UFK Comparison of several methods such as)

all about the signal process in matlab file

控制规律的选用要根据过程特性和工艺要求来选取，决不是说PID控制规律在任何情况下都具有较好的控制性能，不分场合都采用是不明智的。如果这样做，只会给其它工作增加复杂性，并给参数整定带来困难。当采用PID控制器还达不到工艺要求，则需要考虑其它的控制方案。如串级控制、前馈控制、大滞后控制等。 (Choice of control law according to process characteristics and process requirements to select, does not mean that PID control law in any case have a better control performance, are used regardless of where it would be unwise. If you do, will only add to the complexity of other work, and to the parameter tuning difficult. When using the PID controller has not yet reached technological requirements, you need to consider other control options. Such as cascade control, feedforward control, large time delay control.)

在MATLAB平台，实现利用禁忌搜索算法，解决基站选址问题(In the MATLAB platform, use the so called tabusearch algorithm to solve the problem of secleting the best base station locations)

Implemented Gamma correction using matlab

语音信号进行分析、处理和设计。用MATLAB做变声器的原理是通过改变输入声音频率，进而改变声音的音色、音调，使输出声音在感官上与原声音不同。变声器是借助对声音音色和音调的双重复合改变，实现输出声音的改变。通过自己发声，共振峰频率的改变是基于重采样实现的(Speech signal analysis, processing and design. MATLAB do voice-changer works by changing the input frequency of the sound, and then change the sound of the tone, pitch, so that the output audio with the original sound in different senses. Voice Changer is the use of sound timbre and tone of the dual composite changes, realize the output sound changes. Through their own sound, formant frequency change is implemented based on resampling)

采用BP算法来实现压缩感知的信号重构示例。BP算法由线性规划来实现，稀疏基为DCT基，信号为语音信号(an example of using BP algorithm for signal reconstruction in compressed sensing. BP algorithm is implemented by linear programming, sparse basis is the DCT basis, the signal used is speech)

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.)