One of the main computer-learning tools is an (artificial) neural network (NN); based on the values y (p) of a certain physical quantity y at several points x (p) =(x 1 (p) ,...,x n (p) ), the NN finds a dependence y = f(x1,...,x n ) that explains all known observations and predicts the value of y for other x = (x1,...,xn). The ability to describe an arbitrary dependence follows from the universal approximation theorem, according to which an arbitrary continuous function of a bounded set can be, within a given accuracy, approximated by an appropriate NN.
Reliable Computing – Springer Journals
Published: Oct 14, 2004
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