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Autonomous and adaptive procedure for cumulative failure prediction

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Abstract

An autonomous adaptive reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available failure time data, Fuzzy Min–Max algorithm is used to globally optimize the number of the k Gaussian nodes. This technique allows determining and initializing the k-centers of the neural network architecture in an iterative way. The user does not have to define arbitrary some parameters. The optimized neural network architecture is then iteratively and dynamically reconfigured as new failure occurs. The performance of the proposed approach has been tested using sixteen real-time software failure data.

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Notes

  1. This dataset is available on: https://www.thedacs.com/databases/sled/swrel.php.

  2. This dataset is available on: https://www.thedacs.com/databases/sled/swrel.php.

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Authors and Affiliations

  1. Laboratoire d’Automatique du CNAM, 75003, Paris, France

    Ryad Zemouri

  2. Automatic Control and Micro-Mechatronic Systems Department, FEMTO-ST Institute, UMR CNRS 6174—UFC/ENSMM/UTBM, 25000, Besançon, France

    Noureddine Zerhouni

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  1. Ryad Zemouri
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  2. Noureddine Zerhouni
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Correspondence to Ryad Zemouri.

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Zemouri, R., Zerhouni, N. Autonomous and adaptive procedure for cumulative failure prediction. Neural Comput & Applic 21, 319–331 (2012). https://doi.org/10.1007/s00521-011-0585-7

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