MATLAB-intelligent-algorithm-source

说明：  对于全方位的识别和优化做了很详细的分析程序处理和样本对照(For the full identification and optimization of doing a very detailed analysis of the samples were processed and)

常规波束形成，一个期望信号，一个干扰，高斯白噪声(the CBF simulation)

roots -Polynomial roots Syntax r = roots(c) Description r = roots(c) returns a column vector whose elements are the roots of the polynomial c. Row vector c contains the coefficients of a polynomial, ordered in descending powers. If c has n+1 components, the polynomial it represents is . Tips Note the relationship of this function to p = poly(r), which returns a row vector whose elements are the coefficients of the polynomial. For vectors,roots and poly are inverse functions of each other, up to ordering, scaling, and roundoff error.

绘制莱斯分布，本程序简单明了，并且可以修改参数(Draw Rice distribution, this procedure is simple, and you can modify the parameters)

施瓦茨变换，用于半无穷大平面的变换，matlab代码(Schwartz transform for the conversion of semi-infinite plane, matlab code)

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.)

This is the code of indian Game KYA AAP PAANCHVI PAAS SE TEZ HAI! add more questions to expand the game by using the same structure copy and paste.(MATLAB code)

这是一本有关MATLAB的参考书，适合于使用MATLAB或正在打算使用MATLAB的读者(This is a reference book on MATLAB, suitable for use MATLAB or readers are going to use MATLAB)

说明：  共振稀疏分解常用算法匹配追踪法，用于轴承故障的分离(Resonance sparse decomposition algorithm matching tracking method is commonly used for bearing fault separation)

This code is a solution for TSP problem with tabu search.