搜索资源列表
FastICA_2.2
- fast fixed-point ICA算法的Matlab实现-fast fixed-point algorithms ICA Matlab
FIxICaarticle
- 几篇著名的定点ICA论文（英文），包括提出算法及应用。-few famous sentinel ICA paper (English), including algorithms and applications.
Classification-MatLab-Toolbox
- 模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值-pattern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
CCECE05_ICA_Code_v10
- ICA codes for different algorithms
ICA-code
- 关于ICA分类算法的经典代码加注释，用于数据挖掘和隐私保护方面-algorithms,,codes about ICA,whith can be used in data mining
bieying
- ICA（主分量分析）算法和程序，相控阵天线的方向图（切比雪夫加权），一些自适应信号处理的算法。- ICA (Principal Component Analysis) algorithm and procedures, Phased array antenna pattern (Chebyshev weights), Some adaptive signal processing algorithms.
qeifing_v71
- ICA（主分量分析）算法和程序，一些自适应信号处理的算法，LDPC码的完整的编译码。- ICA (Principal Component Analysis) algorithm and procedures, Some adaptive signal processing algorithms, Complete codec LDPC code.
Classification-MatLab-Toolbox
- matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值--attern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
盲源分离
- 常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化（ Infomax ）以及独 立成分分析（ ICA ）等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关， 一旦假设的概率密度与实际信号的密度函数相差甚远，分离性能将大大降低。本文提出采用 核函数密度估计的方法进行任意信号源的盲分离，并通过典型算例与几种盲分离算法进行了 性能比较，验证了方法的可行性。(The commonly used blind separation algorithms include
ypea118-imperialist-competitive-algorithm
- 所提出的进化优化算法通常是由自然过程建模和物种进化的其他方面，特别是人类进化的启发而得到的。(The proposed evolutionary optimization algorithms are generally inspired by modeling the natural processes and other aspects of species evolution, especially human evolution, are not considered.)