Recurrent neural network model for reheating furnace based on sequential learning with unscented Kalman filter Online publication date: Fri, 07-May-2010
by Ying-Xin Liao, Jin-Hua She, Min Wu
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 2, No. 3, 2010
Abstract: In order to model the dynamics of a walking beam reheating furnace, a multi-input multi-output recurrent neural network (RNN) is constructed based on a sequential learning algorithm. The learning algorithm employs growing and pruning criteria based on the concept of significance of hidden neurons to achieve a compact network. An unscented Kalman filter (UKF) is used to improve the learning accuracy by estimating the parameters of the RNN from incomplete and noisy measurements. Unlike existing methods, this one uses a vector instead of a scalar to denote the width of the allocated neuron so as to precisely represent the probability distributions of different input variables. The effectiveness of the RNN combined with the UKF is compared with that of the RNN with an extended Kalman filter (EKF), and the results show that the former estimates the temperatures of zones of the furnace with a higher precision than the latter.
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