Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural Computation
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
Neural Network Model for Maximum Ozone Concentration Prediction
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Architecture of Systems Problem Solving
Architecture of Systems Problem Solving
Learning to Forget: Continual Prediction with LSTM
Neural Computation
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
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The prediction of ozone levels is an important task because this toxic gas can produce harmful effects to the population health especially of children. This article describes the application of the Fuzzy Inductive Reasoning methodology and a Recurrent Neural Network (RNN) approach, the Long Short Term Memory (LSTM) architecture, to a signal forecasting task in an environmental domain. More specifically, we have applied FIR and LSTM to the prediction of maximum ozone(O3) concentrations in the East Austrian region. In this article the results of FIR and LSTM on this task are compared with those obtained previously using other types of neural networks (Multilayer Perceptrons (MLPs), Elman Networks (ENs) and Modified Elman Networks (MENs)). The performance of the best LSTM networks inferred are equivalent to the best FIR models identified and both are slightly better than the other Neural Networks studied (MENs, ENs and MLPs, in decreasing order of performance). Cross validation tests are included in this research in order to study more deeply the accuracy of the FIR models and to extract as much information as possible from the available data.