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zero-crossing
this program finds the number of zero crossings in a given signal.
- 2013-11-09 22:48:41下载
- 积分：1
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zengliangshiPID
说明： 增量式PID算法的matlab仿真程序，在其中还加入了时滞环节(Incremental PID algorithm matlab simulation program, in which also joined the time-delay link)
- 2008-10-02 14:23:41下载
- 积分：1
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88
说明： UWB的仿真代码，还算是比较详细的，希望对您有所帮助(UWB simulation code, fairly detailed, and I hope for your help)
- 2012-12-29 11:08:04下载
- 积分：1
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高阶滑模观测器的MATLAB仿真源文件 observe
本代码为高阶滑模观测器的MATLAB仿真源文件，希望大家下载学习。(The code for the high order sliding mode observer of MATLAB simulation source file, hope everyone to download learning.)
- 2020-06-27 00:20:01下载
- 积分：1
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MATLAByingyongchengxu
说明： 独到精辟的算法及例子，通过MATLAB语言由浅入深的介绍了控制系统的仿真(Original algorithm and brilliant examples MATLAB language through easy-to-digest introduced control system simulation)
- 2008-12-08 10:32:08下载
- 积分：1
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documentimportnode11
My Failing Certificate extends My Certificate.
- 2014-02-25 10:51:30下载
- 积分：1
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Polynomial
polynomial functions
- 2016-08-16 18:09:06下载
- 积分：1
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yy
说明： matlab数学形态学处理里面，一维信号的膨胀腐蚀等没有现成的公式，编程为解决一维信号的处理问题。(inside matlab mathematical morphology, the expansion of one-dimensional signal no ready formula for corrosion, programming to address the problem of one-dimensional signal processing.)
- 2011-04-11 15:53:07下载
- 积分：1
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OFDM[1]
很好的一个ofdm算法的源代码，很适合大部分的初学者。有利于大家好好的理解ofdm的功能。(Ofdm algorithm a good source code, it is suitable for most beginners. Be kind to help understand the functions of ofdm.)
- 2010-05-12 15:50:15下载
- 积分：1
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bootgmregress
自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。
在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。
二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。
BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods.
Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs.
Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)
- 2011-05-21 13:19:38下载
- 积分：1
