create_fit_net

此文件是引文原版的matlab programming 它包括以下内容 Creating and Concatenating Matrices Accessing Elements of a Matrix Getting Information About a Matrix 。。。。。 总之包括所有matlab的使用方法(This file is the original citation of the matlab programming which includes the following Creating and Concatenating MatricesAccessing Elements of a MatrixGetting Information About a Matrix. . . . . All in all, including the use of all of matlab)

说明：  这是建立F16的非线性simulink模型，并且包含数据(This is the establishment of nonlinear simulink model F16, and includes data)

SPICE的代码，可以运行基于相关矩阵的估计方法，这个方法可以应用于稀疏求解，例如求解DOA(the matlab code for SPICE, it s a sparse covariance-based method for sparse solution, e.g.,the solution for the estimation of DOA)

该书基于matlab编程来实现阵列天线方向图的形成(The book based on the matlab programming to achieve the formation of array antenna pattern)

本文首先对各种通信信号进行了理论分析，在此基础上针对数字调制信号采用了基于决策论方法的调制方式识别算法，并利用MATLAB软件进行了计算机仿真。本文的调制识别算法是基于决策论方法，该方法具有运算量小，识别效果好等优点(Firstly, the various communication signals are analyzed theoretically, on the basis adopted for digital modulation method based on decision theory, modulation recognition algorithm, and using MATLAB software, the computer simulation. This modulation recognition algorithm is based on the decision theory method, which has the computational load, and good recognition results)

说明：  本程序实现多模态故障检测与故障诊断，用的方法是pca_GMM_FJ，对象为TE,GMM是用的FJ算法，效果很好(This procedure realize multimodal, fault detection and fault diagnosis method is pca_GMM_FJ, object for TE, GMM FJ algorithm is used, the effect is very good)

这个是对PL-logdist和PL-lognormal的一个仿真。程序简单易懂，但是意义不小。有专门的画图子程序。(This is a PL-logdist and PL-lognormal a simulation. Procedures are simple, but the meaning is not small. Have a special drawing routines.)

基于最小风险的贝叶斯分类器，用于数字分类识别(Bayesian minimum risk classification for digital classification)

赫尔辛基科技大学有关于盲信号去噪的的matlab工具箱，非常好用。希望对大家有用(about the denoise matlab blind source separation from hitu。it is very cool got it!)

四种不同的算法在实现多项式相乘时的时间复杂度比较(Four different algorithms, when multiplied in the realization of polynomial time complexity comparison)