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蒙特卡罗算法随机试验计算圆的面积的全过程(Monte Carlo algorithm for the whole process of the randomized trials calculated circle area)

这个小程序是计算涡轮一级的反动度沿叶高分布的，只要有压力沿叶高分布的.csv文件即可(This program can calculate the reaction in the turbine, it only needs the pressure distribution along radial direction.)

a mathematical framework for the asymptotic analysis of this system in generic noise and interference and also we investigate the performance of cooperative relaying over fading channel for an LTE downlink session

Algorithm for Frequency estimation

说明：  有关LTE信道估计的硕士论文。主要讲上行信道的估计算法和实现(Master s thesis for LTE channel estimation)

说明：  MIMO系统的VBLAST仿真程序，请执行blast_MMSE_IC.m(VBLAST MIMO system simulation program, the Executive blast_MMSE_IC.m)

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.)

matlab中求解最大最小问题的蚂蚁算法源代码。很有参考价值。(Matlab solution of the biggest problems the smallest ant algorithm source code. Great reference value.)

说明：  S函数的BP神经网络PID控制器Simulink仿真(S function of BP neural network PID controller Simulink simulation)

模拟退火的matlab版本，非常有用，需要的下(Simulated annealing matlab version, very useful at need)