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Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

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Abstract

This paper presents a new parallel architecture of the Leven-berg-Marquardt (LM) algorithm for training fully connected feedforward neural networks, which will also work for MLP but some cells will stay empty. This approach is based on a very interesting idea of learning neural networks without error backpropagation. The presented architecture is based on completely new parallel structures to significantly reduce a very high computational load of the LM algorithm. A full explanation of parallel three-dimensional neural network learning structures is provided.

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Correspondence to Jarosław Bilski .

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Bilski, J., Wilamowski, B.M. (2017). Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-59063-9_3

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