This paper describes the application of artificial neural networks to acoustic-to-phonetic mapping. The experiments described are typical of problems in speech recognition in which the temporal nature of the input sequence is critical. The specific task considered is that of mapping formant contours to the corresponding CVC' syllable. We performed experiments on formant data extracted from the ...

This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...

This study aimed to establish a machine learning (ML)-based rice blast predicting model decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature solar energy were selected for development. developed multilayer perceptron (MLP), support vector (SVM), Elman recurrent neural network (Elman RNN...

The complex and unique topography of karst regions highlights the weaknesses traditional hydrological models which fail to fully generalize them. successive proposals standard artificial intelligence (AI) models, pre-processing techniques, post-processing methods have provided new opportunities enhance accuracy runoff prediction in areas. In this study, first, BP neural network model Elman were...

Simple recurrent networks were trained with sequences of phonemes from a corpus of Turkish words. The network's task was to predict the next phoneme. The aim of the study was to look at the representations developed within the hidden layer of the network in order to investigate the extent to which such networks can learn phonological regularities from such input. It was found that in the differ...