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See the tear off sheet for notes on back propagation

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Unformatted text preview: can leave your solution expressed as a product, and it may help us assign partial credit. 9 Part C (10 points) Your friend is building a device to unlock a door when it hears a secret knock pattern, and realizes that a neural network could do it, if only it had a sense of timing. You suggest feeding the output of one neuron into the input of one of its ancestors, and thereby get a dependence on timing. 1. You think about training the network by standard back-propagation, but decide that you can't. Why? The solution is clear: Genetic Algorithms! You'll set up a population of identical neural networks with random weights, you discretize your input every 100 milliseconds into a sequence k1...kn of 0 if silence and 1 if a knock was heard, ensuring that k1 is always 1, and timing out eventually. You'll choose the fittest few neural networks at each step. Your friend jots down a few ideas for fitness functions: A. Whether the full knock pattern was correctly classified B. The length of the subsequence k1 … kt that is correctly cl...
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