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matlab基础与应用实例集粹配套光盘内容 值得学习(matlab basic and application examples Base of supporting CD-ROM content worth learning)

Fourier transform and DFT (Fourier transform)

本程序是基于Matlab开发的，描述了一个机器人自动寻找最优路径的问题，使之得到了很好的解决。(This program is developed based on Matlab, describes a robot automatically finds the optimal path problem, so that it can be a good solution.)

傅里叶变换对信号的处理和分析，测试傅里叶频谱(Fourier transform of the signal processing and analysis, test the Fourier spectrum)

说明：  BP，基于压缩传感的基追踪重建算法，能够较好实现压缩数据的还原(BP, the base track reconstruction algorithm based on compressed sensing)

DTMF报告和代码，基于simulink仿真，通过GUI调用m代码和simulink，可加噪音。(DTMF reports and code, based on simulink simulation, call m code and simulink through GUI, can add noise.)

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.)

利用PHD算法对函数的频率进行估计并画出图形(you can obtain frequency of the function by the PHD algorithm and get the figure)

用matlab语言实现32阶FIR滤波器，并实现气频域和时域仿真(Matlab language with 32-order FIR filter, and to achieve air frequency domain and time domain simulation)

Basic Matlab book for student 3rd