dibangzhuangpushe

fcm algorithm using pso

针对弧齿锥齿轮的matlab编程，不过中间还有些问题(Matlab programming for spiral bevel gears, but the middle still have some questions)

该代码是根据基于流行正则化和后验概率的分类模型而改写的，能够对复杂数据进行准确的分类。(The code is based on the popular regularization and posterior probability classification model and rewritten, able to accurately classify complex data.)

包括广义互相关函数GCC时延估计，三相光伏逆变并网的仿真，基于kaiser窗的双谱线插值FFT谐波分析，用于信号特征提取、信号消噪，PLS部分最小二乘工具箱,是路径规划的实用方法，构成不同频率的调制信号。( Including the generalized cross-correlation function GCC time delay estimation, Three-phase photovoltaic inverter and network simulation, Dual-line interpolation FFT harmonic analysis kaiser windows, For feature extraction, signal de-noising, PLS PLS toolbox, Is a practical method of path planning, Constituting the modulated signals of different frequencies.)

A new technique of free-space simulation has been developed for solving unbounded electromagnetic problems with the finite-difference time-domain method.

用matlab语言编写，实现分段函数的绘制，已经经过调试(Matlab language used to achieve sub-function mapping, has been commissioning)

说明：  matlab必备的源代码教程，希望能用的到(matlab)

说明：  PM的一种常用的实现方法是用调制信号控制谐振回路或移相网络的电抗或电阻元件以实现PM。 (Generation of time signal from wave spectrum (Jonswap,PM,etc.) and re-build wave spectrum.very good，you have look for best for your study!I hope help you to the job Simple program for time signal generation from wave spectrum, can be applied for oofshore eng. naval arch. student in model test.)

matlab代码，用于计算表面等离子体与物质的相互作用(for the interaction of SPR and mass)

The early detection of arrhythmia is very important for the cardiac patients. This is done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. These features can be used in the classification of different types of arrhythmias. In this paper, we present three different algorithms of features extraction: Fourier transform (FFT), Autoregressive modeling (AR), and Principal Component Analysis (PCA). The used classifier will be Artificial Neural Networks (ANN). We observed that the system that depends on the PCA features give the highest accuracy. The proposed techniques deal with the whole 3 second intervals of the training and testing data. We reached the accuracy of 92.7083 compared to 84.4 for the reference that work on a similar data.