TDOATaylorAlgorithm

A derangement Recursion written in Python

bfsk的仿真模型 其中包括模型和m文件 运行.m文件(bfsk simulation model including model documents and m running. M documents)

This MATLAB M-file will produce the FDTD solution of a z-directed line source in a two-dimensional TMz computational domain excited

本书对无线信道的建模、OFDM以及MIMO的基础理论和实践作了一个综合的介绍，并给出了MIMO-OFDM系统基本的的技术matlab仿真代码，非常值得从事无线通信系统研究仿真人员作为参考。(MIMO-OFDM is a key technology for next-generation cellular communications (3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE 802.11a, IEEE 802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB, DMB). This book provides a comprehensive introduction to the basic theory and practice of wireless channel modeling, OFDM, and MIMO, with MATLAB programs to simulate the underlying techniques on MIMO-OFDM systems.)

瑞利信道_帧：128个0，128个PN (Rayleigh channel _ Frame: 128 0,128 a PN)

svm-km支持向量机工具箱，特点是他们能够同时最小化经验误差与最大化几何边缘区.因此支持向量机也被称为最大边缘区分类器.(svm-km SVM Toolbox, is characterized by experience that they can while minimizing errors and maximizing geometric border zone. therefore support vector machine is also known as the maximum marginal zone classification.)

Matlab code for Channel Estimation

一个用matlab编的可以动的飞机动画制作小程序（源程序在网上下载，经本人稍加改动），注意六个状态值是需要外部输入的，比较适合做飞行器仿真试验用。(a series of the aircraft can move small animation (downloaded from the Internet source, I is modified), to six state value is a need for external input, more suitable for aircraft simulation experiment.)

基于蚁群算法的改进进行边缘检测，改方法可行，并且速度快，与传统算法进行比较。(Based on improved ant colony algorithm for edge detection, change the method is feasible, and fast, with traditional algorithms were compared.)

Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */