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PCA_NN
- PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的
an_ica_tool
- 一个ICA工具。This binary version of the runica() function of Makeig et al. contained in the EEG/ICA Toolbox runs 12x faster than the Matlab version. It uses the logistic infomax ICA algorithm of Bell and Sejnowski, with natural gradient and extended
FINAL
- attendence project using face recognition system based on pca and lda algorithm
beijei-V5.5
- It contains positional PID algorithm, integral separate PID, There are good reference value, Gabor wavelet transform and PCA face recognition code.
xu500
- PSS primary synchronization signal in the time domain simulation related, Fiber Transmission wireless communication system performance, Combined with PCA scale invariant feature transform (SIFT) algorithm.
jnawj
- You can achieve data classification and regression pattern recognition, When processing a signal frequency analysis, Combined with PCA scale invariant feature transform (SIFT) algorithm.
wbfba
- It contains CV, CA, Single, current, constant turn rate, turning model, By applying the beam forming technology of BER Combined with PCA scale invariant feature transform (SIFT) algorithm.