jiedian

基于熵的检测器，有很好的鲁棒性在噪声不确定的情况下，环境应用认知无线电(Entropy-based detector, has a good robustness under noise uncertainty, environmental applications of cognitive radio)

合成孔径雷达RMA算法的matlab程序(RMA algorithm for synthetic aperture radar)

基于微观交通流建模中运用车辆跟驰模型方法 进行仿真 车辆更新(Use of car-following model method based on microscopic traffic flow modeling simulation vehicle updates)

低压电力线载波通信的OFDM系统仿真，采用了压缩感知的方法进行信道估计，分别选取不同导频数目与LS算法比较，仿真效果良好。(OFDM power line carrier communication system simulation, using the compressed sensing method for channel estimation, respectively, to select a different number of pilot and LS algorithm, the simulation results is good.)

说明：  PRACTICAL 3 CODES CONTROL SYSTEM

说明：  产生30行16列的矩阵，每一行代表一路信号，产生32路信号，对32路信号解复用(Produce a matrix of 30 rows 16 columns, each row represents one signal, resulting in 32 signals, the signal demultiplexing 32)

Spectral analysis of discrete deterministic signals

Li near r egr essi on, acti ve learning We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion where the go al is to pr e dict a con tin uous resp onse y t ∈ R to e ach example ve ctor. Here to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e easily extended (ne xt lecture).

对具有随机噪声的二阶系统的模型辨识，进行标幺化以后系统的参考模型差分方程为 (7.90) 式中， 为随机噪声。由于神经网络的输出最大为1，所以，被辨识的系统应先标幺化，这里标幺化系数为5。利用图7.5正向建模(并联辨识)结构，神经网络选用3-9-9-1型，即输入层i，隐层j包括2级，输出层k的节点个数分别为3、9、9、1个； (Model identification of two order systems with stochastic noise)

ca码的生成，还有跟踪环路的仿真，具有一定的参考价值(ca code generation, as well as tracking loop simulation, has a certain reference value)