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original_101_0.5
matlab program to calculate force.
- 2009-03-26 22:22:28下载
- 积分：1
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DSCDMA
DS CDMA simulation matlab source code
- 2010-01-24 15:53:16下载
- 积分：1
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SERBER
说明： 用matlab 是实现qpsk的方针算法，并计算方针和理论的符号错误率和比特错误率~~请大家指正(Qpsk with matlab is to achieve the policy algorithm, and calculate the principle and theory of symbol error rate and bit error rate ~ ~ please correct me)
- 2010-04-24 09:56:45下载
- 积分：1
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computerwork_2
2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。
1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。
2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。
(Computer Experiments:
1. Consider an AR process x(n) defined by the difference equation
where v(n) is an additive white noise of zero mean and variance .The AR parameters and are both real valued:
a) Calculate the noise variance such that the AR process x(n) has unit variance .Hence , generate different realization of the process x (n).
b) Given the input x (n), an LMS filter of length M = 2 is used to estimate the unknown AR parameters and . The step size is assigned the value 0.05. Compute and plot the ensemble average curve of and by averaging the value of parameters and over an ensemble of 100 different realization of the filter. Calculate the time constant according to the experiment results and compare with the corresponded theoretical value.
c) For one realization of the LMS filter, compute the prediction error
And the two tap-weight errors
and
Using power spectral plots of , show that)
- 2020-06-28 19:40:01下载
- 积分：1
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Sparse-Recovery
稀疏重构的理论基础和数值方法
（英文原版图书）(Theoretical Foundations and Numerical Methods for Sparse Recovery)
- 2013-12-19 16:04:27下载
- 积分：1
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locate
it s about locating TDOA algorithm
- 2013-10-11 19:07:08下载
- 积分：1
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1807.01622
说明： 深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)
- 2020-06-23 22:20:02下载
- 积分：1
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new
基于MATLAB的电力系统潮流计算模块 (MATLAB-based Power Flow Calculation of Power System Module)
- 2009-05-20 10:40:48下载
- 积分：1
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MDPtoolbox
markov decision process (MDP) for Matlab
- 2013-01-04 12:02:48下载
- 积分：1
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MATLAB_Engine-andC-hybrid-
使用MATLAB_Engine与C混合编程(MATLAB_Engine andC hybrid programming)
- 2012-08-23 11:15:45下载
- 积分：1
