Chapter4

generating time varying channel for jakes or flat fading.

heat equation matlab file

搭建OFDM通信系统的框架，matlab程序运行时导入数据文件作为输入参数，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，可以广泛的应用于数据预测及数据分析,FMCW调频连续波雷达的测距测角，计算多重分形非趋势波动分析。( Build a framework OFDM communication system, Import data files as input parameters matlab program is running, Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, Can be widely used in data analysis and forecast data, FMCW frequency modulated continuous wave radar range and angular measurements, Calculate the multifractal trend fluctuation analysis.)

BP算法在单相逆变电源无差拍控制中的应用(BP algorithm in single-phase inverter deadbeat control of)

本程序设计的是咬尾编码的turbo码，用matlab编写。咬尾turbo码也即不需要拖尾比特，使得前后两个状态相同，译码采用修正的MAP译码算法。此文件为咬尾编码的主程序，将数据流分段编码并行处理(This program is designed to encode the turbo tail biting code, written with matlab. Tail biting turbo code that is not trailing bits to make the same before and after the two states, the MAP decoding using modified decoding algorithm. This file is biting the end of the main program code, the encoded parallel data stream segment)

说明：  This is a time series analysis matlab under the ARMA model and prediction procedures. ARMA algorithm by using a series of data to predict and analyze the prediction rule

simulink model of zeta converter. the important thing it is in closed loop using p i controller. it is for study purpose..

ABAQUS UMAT code 适用于平面应变，轴对称的黏弹塑性线性应变硬化材料，适用于大变形。(The ABAQUS UMAT code applies to plane strain, axisymmetric viscoelastic plasticity linear strain hardening material, suitable for large deformation.)

说明：  实现ASK DPSK 调制解调的matlab 仿真(to come true ASK DPSK)

支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。( Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation.)