ANN control based on generalized minimum variance and its application to boiler-burning system

Generally, the training of artificial neural networks (ANNs) involved in a control system can be performed online depending on whether they execute useful work or not while learning is taking place. The aim of this paper is to apply a novel ANN self-tuning controller based on the generalized minimum variance controller and multiplayer neural network architecture to process control by online training the neural emulator and controller. Finally, we introduce the application of the controller to a boiler-burning system.<
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