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Prove that the LMS weight update rule described in this chapter performs a gradient descent to minimize the squared error. In particular, define the squared error E as in the text. Now calculate the derivative of E with respect to the weight wi, assuming that V̂(b) is a linear function as defined in the text. Gradient descent is achieved by updating each weight in proportion to -(∂E)/(∂wi). Therefore, you must show that the LMS training rule alters weights in this proportion for each training ex
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