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fit_mix_gaussian
fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm
format: [u,sig,t,iter] = fit_mix_gaussian( X,M )
input: X - input samples, Nx1 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian
sig - fitted standard deviation for each gaussian
t - probability of each gaussian in the complete distribution
iter- number of iterations done by the function
- 2011-02-09 19:05:43下载
- 积分：1
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QAMJEUDI
modulation en quadrature QAM
- 2014-09-06 19:06:47下载
- 积分：1
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lat
aircraft lateral dynamics evaluation code from a stability and control book. unfortunately the exact name of the book is not known. the file works fine.
- 2011-12-02 19:47:48下载
- 积分：1
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bai-4-mfile
Intelligent control - excercise 4
- 2013-05-01 00:41:20下载
- 积分：1
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新建文件夹
单元刚度矩阵转化为整体矩阵，如何在MATLAB中实现(stiffness matrix THE BELOW ELEMENT MATRICES AND LOAD VECTORS ARE IN THE NODAL COORDINATE SYSTEMS.
GRAVITY AND TRANSIENT EFFECTS ARE INCLUDED.)
- 2019-05-16 16:53:30下载
- 积分：1
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NSGA-III
测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)
- 2021-01-26 22:38:41下载
- 积分：1
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ecg
根据已给ecg数据文件，画出心电图波形，并自动标记出特征点位置，并计算P-R时间间隔
等参数。
附件是.dat为数据文件，为二进制文件，每2个字节代表一个采样点，采样频率1000hz。依
次为1-12导联的第一个采样点，其次是1-12导联第二个采样点，....
.hea头文件是文本格式说明文件，可以看到十二导联存放的先后顺序。
(According to the data file has to ecg, draw the ECG waveform, and automatically mark the feature points, and calculate parameters such as PR interval. The attachment is. Dat for data files, binary files, each 2 bytes represent a sampling point, the sampling frequency 1000hz. Followed by a 1-12 lead in the first sampling point, followed by 1-12 second sampling point lead ,..... Hea header file is a text file format that you can see the sequence)
- 2010-12-30 10:49:58下载
- 积分：1
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linear-category
本程序主要是对线性判别函数分类器进行实现。(Linear discriminant function classifier achieved.)
- 2010-07-21 15:48:05下载
- 积分：1
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getPDF2
In this paper, we investigate the timing and carrier
frequency offset (CFO) synchronization problem in decode and
forward cooperative systems operating over frequency selective
channels. A training sequence which consists of one orthogonal
frequency-division multiplexing (OFDM) block having a tile
structure in the frequency domain is proposed to perform synchronization.
Timing offsets are estimated using correlation-type
algorithms. By inserting some null subcarriers in the proposed tile
structure, we propose a computationally efficient subspace decomposition-
based algorithm for CFO estimation. The issue of optimal
tile length is studied both theoretically and through simulations.
By judiciously designing the tile size of the pilot, the proposed
algorithms are shown to have better performance, in terms of
synchronization errors and bit error rate, than the time-division
multiplexing-based training method and the computationally demanding
space-alternating generalized expectation-maximization
- 2010-08-11 18:46:04下载
- 积分：1
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基于粒子群优化算法的特征选择SVM分类
说明： 针对“BreastCancer”数据集，作为对比，第一次对特征集直接进行SVM分类，第二次使用粒子群算法进行特征选择后再进行SVM分类。并且对比和分析了两次分类的结果。(For "BreastCancer" data set, as a comparison, the first time the feature set is directly classified by SVM, and the second time the feature set is selected by particle swarm optimization before SVM classification. The results of the two classifications are compared and analyzed.)
- 2021-03-05 21:39:31下载
- 积分：1
