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利用MATLAB计算矩阵并且实用PID控制来实现比例环节的控制
(MATLAB calculations and practical use of PID control matrix to achieve a proportional control)
- 2010-06-20 17:01:04下载
- 积分：1
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MSK
MSK信号的调制及解调,包含三种解调方法，每种方法都给出了误码性能模拟。含有以下文件：
MSK.m
MSK_0.m
MSK_2.m
MSK_3.m
delay.m
difference.m
rand_binary.m
(MSK modulation and demodulation of signals, including three demodulation method, each method are given BER performance simulation. Contains the following files:
MSK.m
MSK_0.m
MSK_2.m
MSK_3.m
delay.m
difference.m
rand_binary.m
)
- 2021-02-22 18:49:41下载
- 积分：1
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LAp
直接由声级计采集的瞬时声压级计算A声级及其1/3倍频程(Directly calculated with the instantaneous pressure level sound level meter to collect A levels and 1/3 octave)
- 2014-05-14 11:34:30下载
- 积分：1
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Matlab80anwser
MATLAB初学者容易碰到的问题的相关解答，适合初学者的学习。(MATLAB for beginners easy to encounter problems related to answer, for beginners to learn.)
- 2010-09-06 22:11:15下载
- 积分：1
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通过 matlab 编程实现捷联惯导系统初始对准、惯导解算的全过程
通过 matlab 编程实现捷联惯导系统初始对准、惯导解算的全过程。
1）初始自对准：利用前10分钟实验车静止的数据进行惯导系统的自对准。采用前2分钟数据进行解析粗对准，后8分钟数据用五状态Kalman滤波器实现精对准；为了验证精对准性能，可在粗对准结果上把航向角人为加一定幅度（如10度）的误差，观察精对准的收敛性能。
2）纯惯导解算：对准结束后转入纯惯性导航解算，要求进行双子样的圆锥和划摇补偿，即每20ms完成一次导航解算，输出完整的导航结果包括三个姿态、两个水平速度和两个水平位置（天向通道发散不必输出），并给出姿态、速度、位置随时间的变化曲线以及位置误差曲线。(The whole process of initial alignment and inertial navigation solution of strapdown inertial navigation system is realized by matlab programming.)
- 2021-04-21 11:18:49下载
- 积分：1
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Jafarkhani
Jafarkhani differential STBC matlab procedure, at different signal to noise ratio, the system compares the SER and BER. The use of 2-fat one admission, Rayleigh fading channel
- 2010-08-02 15:14:49下载
- 积分：1
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Kalman
该文档给出了KALMAN滤波的MATLAB语言实现程序，希望对初学者有所帮助(The document gives the KALMAN filter MATLAB language program, want to help beginners)
- 2010-05-10 16:42:59下载
- 积分：1
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RANSAC-Toolbox
一个适用于Matlab的随机抽样一致算法(RANdom SAmple Consensus,RANSAC)工具箱(RANSAC(RANdom SAmple Consensus)Toolbox for Matlab)
- 2012-04-01 10:54:37下载
- 积分：1
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C-MAPSS-Data
涡轮风扇发动机从正常运行至失效的全寿命采集的实验数据，对于研究涡轮风扇发动机的故障的预测及性能评估想必有用。(Turbofan engine failure from normal operation to the whole life of the experimental data collected for the study of turbofan engine failure prediction and performance evaluation must be useful.)
- 2013-08-11 13:07:20下载
- 积分：1
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KNN
Implement the K nearest neighbor algorithm by your own instead of using available software.
2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result.
3. Train the classifier using your training dataset, and test the classifier using your testing dataset.
4. Repeat the experiment (Step 2 and Step 3) 30 times. For each time, you need to record the training data accuracy and testing data accuracy. Finally, you can obtain the average training data accuracy and average testing data accuracy.
- 2013-05-11 11:46:54下载
- 积分：1
