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A new learning algorithm for multi-layer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive techniques, the effect of the RPROP adaptation process is not(More)
ENZO-M combines two successful search techniques using two diierent timescales: learning (gradient descent) for netuning of each oospring and evolution for coarse optimization steps of the network topology. Therefore, our evolutionary algorithm is a metaheuristic based on the best available local heuristic. Through training each oospring by fast gradient(More)
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