Convergence and Robustness Analysis of the Exponential-Type Varying Gain Recurrent Neural Network for Solving Matrix-Type Linear Time-Varying Equation

Zhang, Zhijun; Zhuo, Fu; Zheng, Lunan; Gan, Min

doi:10.1109/access.2018.2873616

Cited by 11 publications

(2 citation statements)

References 33 publications

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“…enhanced ML technique. RNN is a supervised ML method designed using three layers, namely, input, hidden, and output layers 29 . RNARX is a prominent subgroup of RNN that uses one or more feedback loops to address complex and time-series problems 30 .…”

Section: Experiments and Data Development A Test Bench Model Was Estmentioning

confidence: 99%

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan

Lipu

Hussain

et al. 2020

Sci Rep

184

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State of charge (Soc) is a crucial index used in the assessment of electric vehicle (eV) battery storage systems. thus, Soc estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise Soc assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced Soc estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced Soc estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

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Section: Experiments and Data Development A Test Bench Model Was Estmentioning

confidence: 99%

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan

Lipu

Hussain

et al. 2020

Sci Rep

184

View full text Add to dashboard Cite

show abstract

“…−Ṁ (t)vec(X (t)) + vec(Ė(t)), (13) where time-variant matrices M (t) ∈ R mn×mn andṀ (t) ∈ R mn×mn are defined as…”

Section: Appendix Bmentioning

confidence: 99%

Performance Analyses of Four-Instant Discretization Formulas With Application to Generalized-Sylvester-Type Future Matrix Equation

et al. 2019

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Zeroing Neural Network Based on Neutrosophic Logic for Calculating Minimal-Norm Least-Squares Solutions to Time-Varying Linear Systems

Katsikis

Stanimirović

Mourtas

et al. 2023

Neural Process Lett

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Convergence and Robustness Analysis of the Exponential-Type Varying Gain Recurrent Neural Network for Solving Matrix-Type Linear Time-Varying Equation

Cited by 11 publications

References 33 publications

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Performance Analyses of Four-Instant Discretization Formulas With Application to Generalized-Sylvester-Type Future Matrix Equation

Zeroing Neural Network Based on Neutrosophic Logic for Calculating Minimal-Norm Least-Squares Solutions to Time-Varying Linear Systems

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