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pso-MATLAB
matlab 开发的标准粒子群算法 用于优化计算(matlab development of standard particle swarm algorithm for optimizing calculation)
- 2010-08-23 11:49:24下载
- 积分：1
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pldacode
This code is useful for PLD measurement
- 2013-07-31 03:00:27下载
- 积分：1
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cylinder
管道内圆柱绕流程序，使用格子玻尔兹曼方法(Channel flow past a cylinderical obstacle, using a LB method)
- 2012-09-02 16:26:38下载
- 积分：1
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mosaic
mosaic.rar拼接技术，matlab 源程序，希望有所帮助，(mosaic.rar splicing technology, matlab source code, hope that helps,)
- 2013-12-24 11:39:29下载
- 积分：1
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Quantity_SNR_Compare
均匀量化和非均匀量化在性能上存在着显著的差异，这种差异可以用信号通过量化器后的量躁比来反映出来。本程序用曲线表示了理论和实际两个方面下两种量化的量躁比。从而可观察他们的性能上的差别。(There are significant differences with the function between Uniform quantization and non-uniform quantization ,which can be quantified by SNR from the quantizer.This program use the curves to denote the SNR of theory and practical signal.Then we can observe the performance of the differences.)
- 2009-11-10 15:55:41下载
- 积分：1
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BOT_for_paper
扩展卡尔曼与质点滤波在纯方位跟踪应用,扩展卡尔曼与质点滤波在纯方位跟踪应用(Bearing only tracking)
- 2012-03-28 19:50:10下载
- 积分：1
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wavelet
应用matlab进行图片去噪，简单的matlab程序。(The engineering image denoising is realized with matlab software system.)
- 2012-05-15 19:10:20下载
- 积分：1
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zishiyingGPC
自适应GPC算法，当被控对象参数未知或慢时变时，采用参数估计算法，在线估计出被控对象的参数，然后用参数估计值代替真实值进行控制律的推导。(Adaptive GPC algorithm, when the charged object is unknown or slowly time-varying parameters, the use of parameter estimation algorithms, on-line estimate the parameters of the controlled object, then use the parameter estimates instead of real value to the control law derivation.)
- 2011-06-22 21:21:45下载
- 积分：1
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waveCompression
将电力市场数据使用小波分解成为两个长度相等的低频和高频小波，然后可以用于灰色模型的建模和波形模型建模！(The electricity market data using wavelet decomposition into two equal length low and high frequency wavelet gray model can then be used for modeling and waveform modeling!)
- 2013-09-17 21:52:32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
