438758mom_compare_circle_TM

最小距离分类器，matlab环境下，可直接调用(Minimum distance classifier, matlab environment, can be directly invoked)

Gaussian Mixture Model test

实现最速上升梯度算法的matlab函数，求得目标函数的最大值(Gradient algorithm to achieve the most rapid rise)

单变量系统的隐式广义预测控制算法的研究,提供了matlab环境下的。m文件(programme for GPC)

说明：  关于机器人网格仿真的一点资料，感兴趣的可以看看(Robot simulation on the grid point data of interest to look at)

用于压缩感知信号重建的正则化自适应匹配追踪算法.pdf(Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Based on Compressive Sensing)

Provide interface to send ELS CT FC frames.

信号处理的Fast ICA实现进行快速独立分量分析(Fast ICA signal processing to achieve fast independent component analysis)

盲源分离算法，该算法能够从混合信号中分离出原始信号。已用MATLAB验证：程序从正弦信号和随机噪声信号的混合信号中成功地分离出了正弦信号和噪声信号。程序保证能正确运行。(Blind source separation algorithm, which can be separated from the mixed signal the original signal. Has been verified with MATLAB: Program from the sinusoidal signal and random noise signal, mixed-signal successfully isolated sinusoidal signal and noise signal. Program guaranteed to run correctly.)

Algorithm Given are P training pairs {X1,d1,X2,d2....Xp,dp}, where Xi is (n*1) di is (n*1) No of Categories=R. i=1,2,...P Yi= Augmented input pattern( obtained by appending 1 to the input vector) i=1,2,…P In the following, k denotes the training step and p denotes the step counter within the training cycle Step 1: c>0 , Emin is chosen, Step 2: Weights are initialized at w at small values, w is (n+1)*1. Counters and error are initialized. k=1,p=1,E=0 Step 3: The training cycle begins here. Input is presented and output computed: Y=Yp, d=dp Oi=f(wtY) for i=1,2,….R Step 4: Weights are updated: wi=wi+1/2c(di-oi)Y for i=1,2,…..R. Step 5: Cycle error is computed: E=1/2(di-oi)2+E for i=1,2,…..R. Step 6: If p<P then p=p+1,k=k+1, and go to Step 3: Otherwise go to Step 7. Step 7: The training cycle is completed. For E=0,terminate the training session. Outputs weights and k. If E>0,then E=0 ,p=1, and enter the new training cycle by going to step 3.