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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,...
In this paper, we attempt to address the architectural design of ELM regressor by applying a constructive method on the basis of ELM algorithm. After the nonlinearities of ELM network are fixed by randomly generating the parameters, the network will correspond to a linear regression model. The selection of hidden nodes can then be regarded as a subset model selection in linear regression. The proposed...
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