SVMRFE

谱估计中的MUSIC算法 仿真，简单明了，对学习谱估计的方法也很大的作用(MUSIC spectral estimation algorithm in the simulation, simple and clear, the learning method of spectral estimation is also a great role)

分布式LT码（DLT）的度分布函数的线性规划，线性规划出中继的度分布函数(DLT linear program)

这本书是关于matlab文件操作和txt等文件转换的资料，希望对大家有用，有好的资料也请大家上传上来 谢谢～(err)

对MIMO-OFDM的信道估计，系统仿真，matlab源程序(On MIMO-OFDM channel estimation, system simulation, matlab source)

采用Matlab工具箱函数建立神经网络，对一些基本的神经网络参数进行了说明，深入了解参考Matlab帮助文档。 (Using Matlab toolbox functions to establish neural networks, some of the basic neural network parameters are described to understand the reference to Matlab help documentation.)

说明：  内含OFDM 峰均比的仿真程序，选择性映射slm和部分序列pts(OFDM PAPR includes simulation program, selective mapping and partial sequence pts slm)

LTE-A异构网的能效仿真研究.pdf (LTE-A can emulate true heterogeneous network research.pdf)

This paper presents a new and efficient approach for capacitor placement in radial distribution systems (RDS) that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors (LSF) are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs Dijkstra Algorithm (DA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately. The proposed method is applied to 33 & 69 RDS. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution. OPTIMAL C

进化差分算法，遗传算法的分支，非常好，值得学习( model and its detailed introduction matlab realize)

极化敏感阵列的误差估计矫正函数 可以实现在误差较大的情况下仍然可以准确参数估计(Polarization sensitive array error correction function can be achieved is estimated in the case of error is larger still be accurate parameter estimation)