This presentation show some applications related to the use of Deep Echo State Network architecture to model and forecast heavy nonlinear processes common in the steel industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions. The results have been compared respect to another common recurrent neural network architecture called LSTM.

Deep Echo State Networks in Industrial Applications

technical paper

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

Stefano Dettori

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Deep Echo State Networks in Industrial Applications

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