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Online sliding-window methods for process model adaptation
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Autores
Orientador(es)
Resumo(s)
Online learning algorithms are needed when the process to be modeled is time varying or when it is impossible to obtain offline data that cover the whole operating region. To
minimize the problems of parameter shadowing and interference, sliding-window-based algorithms are used. It is shown that, by
using a sliding-window policy that enforces the novelty of the data it stores and by using a procedure to prevent unnecessary
parameter updates, the performance achieved is improved over a first-in–first-out (FIFO) policy with fixed interval parameter updates.
Important savings in computational effort are also obtained.
Descrição
Palavras-chave
Adaptive systems Feedforward neural networks Learning systems Modeling Nonlinear systems
Contexto Educativo
Citação
Ferreira, P. M.; Ruano, A. E. Online Sliding-Window Methods for Process Model Adaptation, IEEE Transactions on Instrumentation and Measurement, 58, 9, 3012-3020, 2009.
Editora
IEEE