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On-line sliding-window Levenberg-Marquardt methods for neural network models
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On-line learning algorithms are needed when the process to be modeled is time-varying or when it is impossible to obtain off-line data that covers the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-based algorithms are used. It is shown that, by using a sliding window policy that enforces the novelty of data stored in the sliding window, and by using a procedure to prevent unnecessary parameter updates, the performance achieved is improved over a FIFO policy with fixed parameter updates. Important savings in computational effort are also obtained.
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Contexto Educativo
Citação
Ferreira, P. M.; Ruano, A. E. On-line sliding-window Levenberg-Marquardt methods for neural network models, Trabalho apresentado em 2007 IEEE International Symposium on Intelligent Signal Processing, In Proceedings of the 2007 IEEE International Symposium on Intelligent Signal Processing, Alcala de Henares, Spain, 2007.
Editora
IEEE