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### Design the Hebb function to calculate the weights for a given input/output mapping using the Hebb rule in Matlab using m-language or in C++

INSTRUCTIONS TO CANDIDATES

CMPG-765/CMPT- 465 Neural Networks and Learning Systems Homework-1

1. Design the Hebb function to calculate the weights for a given input/output mapping using the Hebb rule in Matlab using m-language or in C++. The Hebb function shall be robust, so it shall not depend on the length of the input and the size of a learning set. Inputs and desired outputs shall be transferred to the function as parameters. It shall return a weighting vector. (undergrads - 50/100, graduates – 30/100)

2. Using the Hebb function, which you designed, apply the Hebbian learning rule to input/output mappings described by all 16 Boolean functions of 2 variables. Test for each input/output mapping whether the weights obtained by the Hebbian rule implement it or

1. (Required for graduates and extra credit for undergrads). Using the Hebb function, which you designed, apply the Hebbian learning rule to input/output mappings described by all 16 Boolean functions oF  2  variables substituting their inputs (1, -1) à (0.5, -0.3) and (-1, 1) à (-0.5, 0.7), but keeping inputs (1, 1) and (-1, -1) and all initial function values. Test for each input/output mapping whether the weights obtained by the Hebbian rule implement it or

1. Write a brief report with your conclusions. A report should be strictly technical. I may mostly consist of a table of functions, which you tested followed by the row (column) where it should be marked whether a Hebbian weighting vector implements this function. (10/100)

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