Obtaining optimum minimum points of error functions: a neural network approach

An iterative neural network solution for the problem of obtaining the optimal minima of error or cost functions containing many local minima is proposed. Use is made of an adaptive constraint function eliminating local minima previously encountered. It is shown that the method yields very fast response times, and the method is applied to a representative example with simulation results verifying its usefulness and speed.<
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