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Training Cellular Neural Networks with Stable Learning Algorithm

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

In this paper we propose a new stable learning algorithm for Cellular Neural Networks. Our approach is based on the input-to-state stability theory, so to obtain learning laws that do not need robust modifications. Here we present only a theoretical study, letting experimental evidences for further works.

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© 2006 Springer-Verlag Berlin Heidelberg

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Moreno-Armendariz, M.A., Egidio Pazienza, G., Yu, W. (2006). Training Cellular Neural Networks with Stable Learning Algorithm. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_83

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  • DOI: https://doi.org/10.1007/11759966_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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