Using the Perceptron learning rule, Find the weights required to perform the following classifications: 1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and therefore have target value 1. 2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and have target value 0.

Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
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Artificial Neural Network

Using the Perceptron learning rule, Find the weights required to perform
the following classifications:
1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and
therefore have target value 1.
2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and
have target value 0.
Use a learning rate of 1.1, 0 =0.35, and starting weights of 0. Using each
of the training vectors as input, test the responses of the net.
Please submit using model
Transcribed Image Text:Using the Perceptron learning rule, Find the weights required to perform the following classifications: 1. Vectors (1,1,1,1) and (0,1,0,0) are members of the class, and therefore have target value 1. 2. Vectors (1,1,1,0) and (1,0,0,1) are not members of the class, and have target value 0. Use a learning rate of 1.1, 0 =0.35, and starting weights of 0. Using each of the training vectors as input, test the responses of the net. Please submit using model
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