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Weight Adaptation Stability of Linear and Higher-Order Neural Units for Prediction Applications

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 833)

Abstract

This paper is focused on weight adaptation stability analysis of static and dynamic neural units for prediction applications. The aim of this paper is to provide verifiable conditions in which the weight system is stable during sample-by-sample adaptation. The paper presents a novel approach toward stability of linear and higher-order neural units. A study of utilization of linear and higher-order neural units with the foundations on stability of the gradient descent algorithm for static and dynamic models is addressed.

Keywords

  • Linear neural units
  • Higher-order neural units
  • Stability analysis

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Acknowledgements

The project is supported by a research grant No. DSA/103.5/16/10473 awarded by PRODEP and by Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Correspondence to Ricardo Rodriguez-Jorge .

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Rodriguez-Jorge, R., Bila, J., Mizera-Pietraszko, J., Martínez-Garcia, E.A. (2019). Weight Adaptation Stability of Linear and Higher-Order Neural Units for Prediction Applications. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_50

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