1、对观测数据进行中心化，；

2、使它的均值为0，对数据进行白化—>Z；

3、选择需要估计的分量的个数m，设置迭代次数p<-1

4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）；

5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的）

6、用对称正交法处理下W

7、归一化W(:,p)=W(:,p)/norm(W(:,p))

8、若W不收敛，返回第5步

9、令p=p+1，若p小于等于m,返回第4步

剩下的应该都能看懂了

基本就是基于负熵最大的快速独立分量分析算法-1, on the center of the observation data,　2, making a mean of 0, the data to whitening->　Z　3, select the number of components to be estimated m, setting the number of iterations p < -1　4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers)　5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p))　8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm