PSO

利用MATLAB实现PSK系统的仿真 很实用(PSK systems using MATLAB simulation to achieve practical)

matlab 在工程上面的应用是一件麻烦的事，于此，提供一些应用知识(the matlab works above is a troublesome thing, this provides some application knowledge)

CAPM代码，资本资产定价模型在Matlab中的实际应用。(Capital Asset Pricing Model with Missing Data)

独立成分分析算法降低原始数据噪声，部分实现了追踪测速迭代松弛算法，有信道编码，调制，信道估计等。( Independent component analysis algorithm reduces the raw data noise, Partially achieved tracking speed iterative relaxation algorithm, Channel coding, modulation, channel estimation.)

说明：  一个开环的SVPWM控制系统，能稳定地控制电机(SVPWM an open loop control system can control the electrical stability)

Mobile Robot Model + Code + Docs in Matlab

Duda经典教材《Pattern Classification》的全部源码(The source code of Duda s book 《Pattern Classification》)

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.)

基于matlab 的 dft 的 fft 的 是吸纳(the same as before fft dft )

这个函数估计到库默尔与第N系列的广义超几何微分方程解的权力。库默尔的差分方程为： x *的克 （十）+（二- X）的*克 （​ ​ x）的- A *的为g（x）= 0 该代码只有4线长，利用Matlab的向量运算有权采取了第N广义超几何级数的权力的总和。(This function estimates the solution to kummer s differential equation with the first N powers of the generalized hypergeometric series. Kummer s differential equation is given by: x*g (x)+ (b- x)*g (x)- a*g(x) = 0 The code is just 4 lines long, using the power of Matlab s vector operations to take the sum over the first N powers of the generalized hypergeometric series.)