Abstract
This article presents the theorems and lemmas of stability, based on Lyapunov stability theory, for Modular Recurrent Trainable Neural Networks that have been widely used by the authors for the identification and control of mechanical systems.
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Hernández Manzano, S.M., Baruch, I. (2014). Stability of Modular Recurrent Trainable Neural Networks. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_9
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DOI: https://doi.org/10.1007/978-3-319-13650-9_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13649-3
Online ISBN: 978-3-319-13650-9
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