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In this paper, we evaluate the performance of descent conjugate gradient methods and we propose a new algorithm for training recurrent neural networks. The presented algorithm preserves the advantages of classical conjugate gradient methods while simultaneously avoids the usually inefficient restarts. Simulation results are also presented using three different recurrent neural network architectures...
For training algorithms of recurrent neural networks (RNN), convergent speed and training error are always two contradictory performances. In this letter, we propose a normalized adaptive recurrent learning (NARL) to obtain a tradeoff between transient and steady-state response. An augmented term is added to error gradient to exactly model the derivative of cost function with respect to hidden layer...
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