Abstract
Training of recurrent neural networks (RNNs) is known to be a very difficult task. This work proposes a novel constructive method for simultaneous structure and parameter training of Elman-type RNNs using a combination of particle swarm optimization (PSO) and covariance matrix adaptation based evolutionary strategy (CMA-ES). The proposed method allows the imposition of certain stability conditions, which can be maintained throughout the constructive process. The examples reported show a monotonic decrease in training error throughout the constructive process and also demonstrate the efficiency of the proposed method for structure and parameter training of RNNs.
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