15th IEEE Sensors Conference - IEEE SENSORS 2016, Orlando (Estados Unidos de América). 30 octubre - 02 noviembre 2016
Resumen:
Soft Sensors (SSs) have been widely investigated and employed as inferential sensing systems for providing on-line estimations of industrial processes' variables. However, industrial processes suffer from different complex characteristics (e.g. time-variance and non-linearity), being very difficult for the SS models to perform well over time. This paper proposes a SS model using an on-line Extreme Learning Machine (ELM) with Directional Forgetting Factor (DFF) which is able to provide online estimations of variables in industrial processes. The main contribution is that the proposed ELM model has the ability of adapting its architecture over time. For this purpose, it is used the Bordering Method and the Reverse Bordering Method. Experiments demonstrate the performance of the proposed method over the state-of-the-art methods.
Palabras clave: Soft Sensor; Extreme Learning Machines; Neural Network; Variable Forgetting Factor; Adaptive Architecture
DOI: 
https://doi.org/10.1109/ICSENS.2016.7808721
Publicado en IEEE SENSORS 2016, pp: 1-3, ISBN: 978-1-4799-8288-2
Fecha de publicación: 2017-01-09.
Cita:
A.R. de Miranda, T.M. Barbosa, R. Araújo, S. Gomes Soares Alcalá, An on-line extreme learning machine with adaptive architecture for soft sensor design, 15th IEEE Sensors Conference - IEEE SENSORS 2016, Orlando (Estados Unidos de América). 30 octubre - 02 noviembre 2016. En: IEEE SENSORS 2016: Conference proceedings, ISBN: 978-1-4799-8288-2
