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This paper proposes a method for adaptive identification and predictive control using an online sequential extreme learning machine based on the recursive partial least-squares method (OS-ELM-RPLS). OL-ELM-RPLS is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in [1]. Like in the batch extreme learning machine (ELM), in OS-ELM-RLS...
This paper proposes a method for fault detection and replacement of the sensor responsible by the measurement of the burning zone temperature in a rotary cement kiln. The control of the burning zone temperature is crucial for the control of kiln temperature and therefore for the control of the cement quality, pollutant emissions, and consumed energy. However the flying dust within the kiln can block...
This work addresses the problem of controlling unknown and time varing plants for industrial aplications. To deal with such problem several Self-Tuning Controllers with a Proportional Integral and Derivative (PID) structure have been chosen. The selected controllers are based on different methodologies, and some use implicit identification techniques (Single Neuron and Support Vector Machine) while...
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