FNN

passive Anti-islanding method(PJD)

EMD算法，经验模态分解，信号处理与分析方面用到，算法很简单，中文(EMD algorithm, the empirical mode decomposition, signal processing and analysis used, the algorithm is very simple, Chinese)

小波阈值自动选取方案去噪，并用HHT对去噪后的轴承信号进行处理，提取故障特征。(Wavelet threshold de-noising program automatically chooses, for bearing with HHT denoised signal processing, fault features.)

偏微分方程的数值解法的MATLAB程序介绍了应用最为广泛的椭圆型、双曲型、抛物型偏微分方程的数值解法，而且还详细编程实现了每种方程的多种常见数值解法。 附件使用MATLAB编程来实现这些算法。 (The numerical solution of partial differential equations of the MATLAB program, introduced the most widely used elliptic, hyperbolic, parabolic partial differential equations numerical solution, but also details of each program to achieve a variety of common numerical solution of equations. Attachments that use the MATLAB program to implement these algorithms.)

IEEE802.15.4a超宽带仿真通道仿真多径时延估计可以用到(Simulation of ultra-wideband channel emulation IEEE802.15.4a)

Objective of this example is to demonstrate how to design and model adaptive controller, tune and analyse its performance using Simulink® . For this example we have used direct adaptive method called Model Reference Adaptive Controller (MRAC). There are three main elements of this model: Reference Model, Plant Model and Adaptive Controller. Each element and its working is explained in "Adaptive Controller Example.pdf", part of attached folder.

自己编写的LQG/LTR程序，里面附有一片LQG/LTR论文，希望各位能够交流探讨。(LQG/LTR method using MATLAB)

包括svpwm的MATLAB仿真实现的matlab程序和word文本文档说明建立过程(Including svpwm the MATLAB simulation of matlab program and word description of the process of establishing a text document )

This software implements the touch-expand algorithm and demonstrates how it can be applied for shape-from-oriented points reconstruction. The algorithm and its application were suggested in:

功率谱估计是利用有限长的数据估计信号的功率谱，广泛应用于各个领域。功率谱估计主要分为经典谱估计与现代谱估计。常用的经典谱估计方法有周期图法，相关法，周期图的改进法，常用的现代谱估计方法有最大熵谱估计，AR模型，MA模型，ARMA模型。经典谱估计适用于长序列的信号，其主要缺陷是描述功率谱波动的数字特征方差性能差，频率分辨率低，现代谱估计适用于短序列的信号，旨在改善谱估计的分辨率，并将其应用于实际地震资料的谱分析。 (Power spectrum estimation is the use of a finite length data to estimate the power spectrum of the signal, widely used in various fields. Power spectrum estimation is divided into classic and modern spectral estimation spectral estimation. Commonly used methods of classical periodogram spectrum estimation method, the relevant law, the cycle graph Improvement Act, commonly used in modern spectral estimation methods have maximum entropy spectral estimation, AR model, MA model, ARMA model. Classical spectral estimation applied to signal a long sequence, its main drawback is the fluctuation power spectrum describing digital signature variance performance is poor, the frequency resolution is low, modern spectral estimation applied to signal short sequences designed to improve spectral estimation resolution, and The analysis applied to real seismic data spectrum.)