A universal neural net with guaranteed convergence to zero system error

A learning algorithm with guaranteed convergence to zero system error is developed. The algorithm also has high potential to converge fast. The basic idea is to let the net grow when stopping at a local minimum, so that the original local minimum is no longer a local minimum with regard to the new net, and the new net always starts from a point with less error than that in the original local minimum. By this method, the error is guaranteed to decrease until it converges to zero. The technique can also be used to make the error to be as small as desired when the backpropagation algorithm reaches a global minimum which does not achieve zero error due to a lack of sufficient number of nodes. When expanding the neural net, the initial weights of the new node can be selected to maximize the error gradient. A mathematical proof of the guaranteed learning of the universal neural net is given, and numerical examples illustrate its high potential for fast learning.<
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