TP3_binaire

Mimo and wimax simulation

基于MATLAB的维纳滤波和逆滤波复原，在程序中可以看到，维纳滤波器的组成。可以更好的实现逆滤波复原算法。(MATLAB based on Wiener filtering and inverse filtering to recover, the program can be seen, the Wiener filter. We can better achieve the inverse filter restoration algorithm.)

A pv module is modeled in MATLAB/SIMULINK environment with use of mathematical equations that describe pv module s schematic model. This model has 2 inputs named Vpv and insolation and has 2 outputs named Ipv and Ppv. Insolation signal is varying.

在目标被动式跟踪中广泛应用的伪量测变换估计器(PLE)具有良好的误差收敛性。然而由于等价噪声和状态的相关性,该估计器的估计是有偏的。提出的强跟踪滤波器(STF)通过强制白化残差具有自适应地校正估计偏差和迅速跟踪状态变化的能力。STF已经在非线性系统时滞估计、故障诊断与容错控制方面取得了很好的效果。(Passive target tracking is widely used in the pseudo-measurement transform estimator (PLE) has a good error convergence. However, due to the equivalent noise and the relevance of state, the estimator is a biased estimate of. Raised strong tracking filter (STF) has adopted mandatory bleaching residuals adaptively estimated bias correction and rapid tracking of the status change. STF has been estimated in the time-delay nonlinear systems, fault diagnosis and fault-tolerant control has been achieved very good results.)

说明：  利用PTS技术降低峰均比，并作出与未使用降低PAPR技术的CCDF对比图形(Comparing with OFDM system without PAPR reduction techniques,figure the CCDF using PTS technique.)

一个基于离散余弦变换的水印算法的matlab程序（嵌入、提取）(one based on discrete cosine transform the watermark algorithm Matlab (embedding and extraction))

完成了对OFDM（正交频分复用）通信系统从信号输入，编码，发射，解码，求误码率的全过程，压缩包中word文件为系统仿真报告，其他为matlab代码。(Completes the OFDM (Orthogonal Frequency Division Multiplexing) communication system the signal input, encoding, transmitting, decoding, bit error rate of the entire process required)

BER_QPSK QPSK MODULATION ANALYZE IN BER

线性系统的模型降阶 pade降阶算法 线性系统的模型降阶 (failed to translate)

BP神经网络：是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。(BP neural network: a Press the front back propagation algorithm to train multilayer feedforward network is one of the most widely used neural network model. BP network can learn and store large amounts of input- output mode mappings without prior reveal mathematical equations that describe this map. It' s learning rule is to use the steepest descent method, through a reverse spread to constantly adjust the network weights and thresholds, so that the minimum squared error of the network. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer).)