The correlation as cost function in neural networks

It is shown that in time series forecasting by feedforward neural nets with a nonlinear transfer function for the output unit the minimization of the usual cost function, the mean squared error, is not equivalent to the maximization of the correlation. A simple modification of the neural net is proposed which restores the equivalence known from linear regression.<
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