The problem of short-term electric load forecasting (STLF) is considered. A modified architecture of Elman-type recurrent neural network is proposed. It utilizes a special fuzzification layer to deal with quantitative as well as ordinal and nominal data. The second hidden layer of the network consists of standard Rosenblatt-type neurons with sigmoidal activation functions. The context layer is ...

This paper presents a comparative study of state-of-the-art neurocomputing methods applied to several benchmark time series, including the white dwarf light curve. The goal is to determine which of the predictive models work best for data from natural sources. The emphasis is on using a unified methodology for selection of the best architectures among those used for comparison. The specific arc...

This paper describes an approach to predict non-verbal cues from speech-related features. Our previous investigations of audiovisual speech showed that there are strong correlations between the two modalities. In this work we developed two models using different kinds of Recurrent Artificial Neural Networks: Elman and NARX, to predict parameters of activity for head motion using linguistic and ...

Short-term wind prediction over different time steps is vital for wind farms in operation for various applications. Considering the complexity of atmospheric processes governing wind, time series models are preferred over physical models for wind prediction. Artificial neural networks (ANNs), which perform a non-linear mapping between inputs and outputs, provide an alternative approach for wind...

In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model sequence labeling is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as CRFs. In this work we propose a solution far simpler but very effective: an evoluti...

The purpose of this paper is to develop an appropriate artificial neural network (ANN) model of induction motor bearing (IMB) failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the IMB and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop the model using ANN for IMB failure predi...

Recently, artificial neural networks have been successfully applied to various hydrologic problems. This paper proposed flood water level modeling using the Hybrid of Back Propagation Neural Network with Extended Kalman Filter and the Hybrid of Elman Neural Network with Extended Kalman Filter that using the water level data from Sungai Kelang which is located at Jambatan Petaling, Kuala Lumpur....

This paper proposes a neural controller to guide a nonholonomic mobile robot during trajectory tracking. The structure of the controller used consists of two models that describe the kinematical mobile robot system. These models are modified Elman neural networks (MENN) and feed forward multi-layer perceptron (MLP). The modified Elman neural networks model is trained with two stages; off-line a...