文件名称:ganzhiqi2
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- 人工智能/神经网络/遗传算法
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- [Matlab] [源码]
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- 2013-12-05
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3、单计算节点感知器，3个输入。给定3对训练样本对如下所示：
X1 = (1，-2，0，-1) ，d1 =-1；X2 = (0，1.5，-0.5，-1)，d2 = - 1；X3 = (-1，1，0.5，-1)，d3 =1；
设初始权向量W(0)=(0.5,1,-1,0.5)，2η=0.1。注意，x第四列为阈值恒等于-1，权向量中第后个分量为阈值，试根据以上学习规则训练该感知器。
-3, a single compute node sensor, three inputs. Given three pairs of training samples are as follows:
X1 = (1,-2,0,-1), d1 =-1 X2 = (0,1.5,-0.5,-1), d2 =- 1 X3 = (-1,1,0.5,-1 ), d3 = 1
Let the initial weight vector W (0) = (0.5,1,-1,0.5), 2η = 0.1. Note, x fourth as the threshold constant equal to-1, right after the first two components of the vector is the threshold, try to train the perceptron learning rule based on the above.
X1 = (1，-2，0，-1) ，d1 =-1；X2 = (0，1.5，-0.5，-1)，d2 = - 1；X3 = (-1，1，0.5，-1)，d3 =1；
设初始权向量W(0)=(0.5,1,-1,0.5)，2η=0.1。注意，x第四列为阈值恒等于-1，权向量中第后个分量为阈值，试根据以上学习规则训练该感知器。
-3, a single compute node sensor, three inputs. Given three pairs of training samples are as follows:
X1 = (1,-2,0,-1), d1 =-1 X2 = (0,1.5,-0.5,-1), d2 =- 1 X3 = (-1,1,0.5,-1 ), d3 = 1
Let the initial weight vector W (0) = (0.5,1,-1,0.5), 2η = 0.1. Note, x fourth as the threshold constant equal to-1, right after the first two components of the vector is the threshold, try to train the perceptron learning rule based on the above.
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