CMPG-765/CMPT- 465 Neural Networks and Learning Systems Homework-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)
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
(undergrads - 40/100, graduates – 30/100)
(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
(undergrads – 30 extra credit points, graduates – 30/100)
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)
DescriptionIn this final assignment, the students will demonstrate their ability to apply two ma
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