The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Ensembles have been shown to provide better generalization performance than single
models. However, the creation, selection and combination of individual predictors is critical …

This paper investigates the short-term load forecasting (STLF) problem via a hybrid
quantized Elman neural network (HQENN) with the least number of quantized inputs, hourly …

In this paper, a soft computing method, based on a recurrent self-organizing neural network
(RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater …

The training of recurrent neural networks (RNNs) concerns the selection of their structures
and the connection weights. To efficiently enhance generalization capabilities of RNNs, a …

The incremental version of randomized neural networks provides a greedy constructive
algorithm for the shallow network, which adds new nodes through different stochastic …

The maximum power point tracking (MPPT) technique is applied in the photovoltaic (PV)
systems to achieve the maximum power from a PV panel in different atmospheric conditions …

Feedforward neural networks have been established as versatile tools for nonlinear black-
box modeling, but in many data-mining tasks the choice of relevant inputs and network …

This paper proposes a novel hybrid optimization algorithm for simultaneous structure and
parameter learning of Elman-type recurrent neural networks (RNNs) combining the …

In this paper, an efficient self-organizing recurrent radial basis function neural network
(RRBFNN), is developed for nonlinear system modeling. In RRBFNN, a two-steps learning …