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Though several approaches have already been proposed in the literature to evolve neural network topologies for solving a wide range of machine learning tasks, this paper presents an alternative one, capable of evolving arbitrarily connected feed forward neural networks (ACFNNs), including linear and nonlinear neurons. A genetic algorithm is conceived to adjust the topology and also to perform variable...
In this paper a novel neural network architecture for medium-term time series forecasting is presented. The proposed model, inspired on the Hybrid Complex Neural Network (HCNN) model, takes advantage of information obtained by wavelet decomposition and of the oscillatory abilities of recurrent neural networks (RNN). The prediction accuracy of the proposed architecture is evaluated using 11 economic...
The NARX network is a dynamical neural architecture commonly used for input-output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, it is shown that the original architecture...
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