Use the outer product version of Hebb rule learning to find the weight matrix in bipolar form for the bidirectional associative memory network based on the following binary input-output vector pairs: s(1) = (1 0 0 0) 41) = (1 0) s(2) = (1 0 0 1) t(2) = (1 0) s(3) = (0 1 0 0) t(3) = (0 1) s(4) = (0 1 1 0) t(4) = (0 1) b. Using the unit step function (with threshold 0) as the output units' activation function, test the response of your network on each of the input patterns. Describe the results obtained. c. Test the response of your network on various combination of input patterns with "mistakes" or "missing data" (as in Example 3.24). Discuss the results you observe.
Use the outer product version of Hebb rule learning to find the weight matrix in bipolar form for the bidirectional associative memory network based on the following binary input-output vector pairs: s(1) = (1 0 0 0) 41) = (1 0) s(2) = (1 0 0 1) t(2) = (1 0) s(3) = (0 1 0 0) t(3) = (0 1) s(4) = (0 1 1 0) t(4) = (0 1) b. Using the unit step function (with threshold 0) as the output units' activation function, test the response of your network on each of the input patterns. Describe the results obtained. c. Test the response of your network on various combination of input patterns with "mistakes" or "missing data" (as in Example 3.24). Discuss the results you observe.
Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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Question
Use the outer product version of Hebb rule learning to find the weight matrix in
bipolar form for the bidirectional associative memory network based on the following
binary input-output
s(1) = (1 0 0 0) 41) = (1 0)
s(2) = (1 0 0 1) t(2) = (1 0)
s(3) = (0 1 0 0) t(3) = (0 1)
s(4) = (0 1 1 0) t(4) = (0 1)
b. Using the unit step function (with threshold 0) as the output units' activation
function, test the response of your network on each of the input patterns. Describe
the results obtained.
c. Test the response of your network on various combination of input patterns with
"mistakes" or "missing data" (as in Example 3.24). Discuss the results you observe.
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