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LMS

于 2011-12-10 发布 文件大小:1KB
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  1,、设置变量和参量:   X(n)为输入向量,或称为训练样本   W(n)为权值向量   e(n)为偏差   d(n)为期望输出   y(n)为实际输出   η为学习速率   n为迭代次数   2、初始化,赋给w(0)各一个较小的随机非零值,令n=0   3、对于一组输入样本x(n)和对应的期望输出d,计算   e(n)=d(n)-X^T(n)W(n)   W(n+1)=W(n)+ηX(n)e(n)   4、判断是否满足条件,若满足算法结束,若否n增加1,转入第3步继续执行(, set the variables and parameters: X (n) is the input vector, otherwise known as the training sample W (n) for the weight vector e (n) for the deviation d (n) is the desired output y (n) is the actual output η is the learning rate n is the number of iterations)

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