Language Inference Using Elman Networks with Evolutionary Training

A Comparative Study of Evolutionary Algorithms for Training Elman Recurrent Neural Networks to Predict Autonomous Indebtedness

This paper presents a training model for Elman recurrent neural networks, based on evolutionary algorithms. The proposed evolutionary algorithms are classic genetic algorithms, the multimodal clearing algorithm and the CHC algorithm. These training algorithms are compared in order to assess the effectiveness of each training model when predicting Spanish autonomous indebtedness.

Training of Elman networks and dynamic system modelling

Monthly Flow Estimation Using Elman Neural Networks

This paper investigates the application of partially recurrent artificial neural networks (ANN) in the flow estimation for São Francisco River that feeds the hydroelectric power plant of Sobradinho. An Elman neural network was used suitably arranged to receive samples of the flow time series data available for São Francisco River shifted by one month. For that, the neural network input had a de...

Generalization of Elman Networks

The Vapnik Chervonenkis dimension of Elman networks is innnite. Here, we nd constructions leading to lower bounds for the fat shattering dimension that are linear resp. of order log 2 in the input length even in the case of limited weights and inputs. Since niteness of this magnitude is equivalent to learnability, there is no a priori guarantee for the generalization capability of Elman networks.

WRPN: Training and Inference using Wide Reduced-Precision Networks

For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks but also that reducing the precision of activations hurts model accuracy much more than reducing the precision of model parameters. We study schemes to train networks from scratch using reduced-precision activations without hurting the...