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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2020

29.11.2019 | Emerging Trends of Applied Neural Computation - E_TRAINCO

Improved zeroing neural networks for finite time solving nonlinear equations

verfasst von: Jie Jin, Lv Zhao, Mu Li, Fei Yu, Zaifang Xi

Erschienen in: Neural Computing and Applications | Ausgabe 9/2020

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Abstract

Nonlinear equation is an important cornerstone of nonlinear science, and many practical problems in scientific and engineering fields can be described by nonlinear equation in mathematics. In this paper, improved zeroing neural network (IZNN) models are presented and investigated for finding the solutions of the time-invariant nonlinear equation (TINE) and time-varying nonlinear equation (TVNE) in predictable and finite time. Compared with the exponential convergence zeroing neural network (ZNN), the convergence time of the IZNN models is finite and able to be estimated; in addition, the IZNN model is more stable and reliable for solving high-order TVNE. Both of the theoretical and numerical simulation results of the ZNN and IZNN for finding the solutions of the TINE and TVNE are presented to demonstrate the superiority and effectiveness of the IZNN model.
Vorheriger Artikel An adaptive ensemble classification framework for real-time data streams by distributed control systems
Nächster Artikel Evaluating graph resilience with tensor stack networks: a Keras implementation

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Metadaten
Titel
Improved zeroing neural networks for finite time solving nonlinear equations
verfasst von
Jie Jin
Lv Zhao
Mu Li
Fei Yu
Zaifang Xi
Publikationsdatum
29.11.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04622-x

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