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matlab
matlab经典算法的程序，用于数学建模的经典算法及其讲解(classical algorithm matlab program for mathematical modeling of the classical algorithm and explain)
- 2010-08-17 11:06:58下载
- 积分：1
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wawsversion2
用于描述谐波叠加法，可以修改参数已完成要做的叠加！(Used to describe the harmonic superposition method, you can modify the parameters to do the overlay has been completed!)
- 2021-03-26 20:29:12下载
- 积分：1
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G729a_lib
G.729的全名Code Excited Linear Prediction Model (CELP) and Conjugate-Structure Algebraic CELP (CS-ACELP). 共轭结构-代数码激励线性预测编码. (Code Excited Linear Prediction Model (CELP) and Conjugate-Structure Algebraic CELP (CS-ACELP)
- 2015-02-27 19:59:33下载
- 积分：1
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multilevel-inverter-lesson
multilevel inverter theory
- 2014-02-21 22:06:46下载
- 积分：1
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MTCL_MUSIC
说明： 基于MUSIC的MIMO雷达DOA、DOD估计(DOD and DOA estimation for MIMO radar based on MUSIC)
- 2019-03-20 16:55:01下载
- 积分：1
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matlabga
说明： 能够用来求解部分关利用遗传算法的优化模型。。(Relations can be used to solve some of the optimization model using genetic algorithms. .)
- 2011-02-21 13:21:04下载
- 积分：1
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AComP_5_1
说明： 高等通信原理16_QAM symbol sequence of 2000 symbols and plot the constellation(Advanced Communication Theory 16_QAM symbol sequence of 2,000 symbols and plot the constellation)
- 2006-04-10 21:21:54下载
- 积分：1
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Function_Surface_Plot
surface plot for matlab
- 2012-04-24 16:46:27下载
- 积分：1
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智能微电网粒子群优化算法 Smart-Microgrid-PSO
智能微电网粒子群优化算法,微源：光伏、风机、发电机、储能等(Smart Microgrid PSO, micro sources: photovoltaic, wind turbines, generators, energy storage, etc.)
- 2021-03-31 19:49:08下载
- 积分：1
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1807.01622
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
