Numerical Analysis of Modeling Based on Improved Elman Neural Network

Shao, Jie; Li, Wang; Zhao, Weiqian; YaQin, Zhong; Malekian, Reza

doi:10.1155/2014/271593

Cited by 3 publications

(2 citation statements)

References 19 publications

(17 reference statements)

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“…Zhu et al used the output hidden feedback ENN to establish the effective thermal error model of the machining center [22]. Shao et al proposed a modeling method based on an improved ENN, in which neurons in the hidden layer are activated by a set of Chebyshev orthogonal basis functions [23]. Han et al developed a Jordan-plus-Elman NARX RNN (Jordan-Elman network) model [24].…”

Section: Introductionmentioning

confidence: 99%

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Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Zhang

Zhao

et al. 2023

IEEJ Transactions Elec Engng

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Dynamic neural networks (DNNs) are widely used in data‐driven modeling of nonlinear control systems. Due to the complexity of the actual operating nonlinear power systems, rigorous dynamic models are always unknown. DNNs can focus on methods that only use input and output information to establish accurate dynamic models and reduce noise in measured data, which is called data‐driven modeling. The core of the DNN is the feedback with memory function. This paper analyzes the traditional Elman neural network (ENN) and nonlinear auto‐regression with exogenous input (NARX) neural network with different structure feedback structures, and proposes a full feedback dynamic neural network (FF‐DNN) with exogenous input. Eight different kinds of neural networks (including ENN, NARX, etc.) are compared and analyzed. The eight kinds of neural networks are applied on the experimental data of the DC‐AC inverter and the power system of Zhejiang Juchuang Smart Technology Company Park in Wenzhou. The experimental results are used to compare the performance of the data‐driven models established by eight different kinds of neural networks under different noise conditions, verify the robustness and generalization performance of dynamic data‐driven modeling based on FF‐DNN, and demonstrate the feasibility and effectiveness of FF‐DNN in actual power systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Introductionmentioning

confidence: 99%

“…Shao et al . proposed a modeling method based on an improved ENN, in which neurons in the hidden layer are activated by a set of Chebyshev orthogonal basis functions [23]. Han et al .…”

Section: Introductionmentioning

confidence: 99%

Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Zhang

Zhao

et al. 2023

IEEJ Transactions Elec Engng

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Association between maternal hepatitis B virus carrier and gestational diabetes mellitus: a retrospective cohort analysis

Yin

Chen

Yang

et al. 2021

Virol J

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Introduction Given that many pregnant women have chronic hepatitis B virus (HBV) infection and that gestational diabetes mellitus (GDM) is linked to poor maternal and neonatal outcomes, we looked into the relationship between the hepatitis B surface antigen (HBsAg) and GDM to see if a high HBV DNA load is linked to a higher risk of GDM in chronic maternal HBsAg carriers. Materials and methods Our study included 39,539 pregnant women who gave birth at the Third Affiliated Hospital of Guangzhou Medical University in Guangzhou, China, between January 1, 2009, and December 31, 2019. The patients were divided into two groups: HBsAg negative (36,500) and positive (3039). The viral load levels of 1250 HBsAg-positive women who had tested their HBV DNA load during pregnancy were separated into three groups. We utilized univariate and multivariable logistical regression analysis to determine the relationship between maternal chronic HBsAg carrier and GDM. Results Being HBsAg positive was discovered to be an independent risk factor for GDM.Pre-pregnancy Obesity and advanced age were linked to an increased incidence of GDM. Those with a high HBV DNA load (> 106 IU/mL) had a higher risk of GDM than HBsAg-positive women with a low viral load (< 103 IU/mL). Pre-eclampsia and intrahepatic cholestasis of pregnancy (ICP) appeared to be more common in HBsAg-positive women than in uninfected women. Conclusions Being HBsAg positive, advanced age, and pre-pregnancy obesity were all revealed to be independent risk factors for GDM in our study. In HBsAg carrier, pregnant women, a high HBV DNA burden was linked to a greater risk of GDM. Furthermore, being an HBsAg carrier during pregnancy raised the risk of ICP and pre-eclampsia.

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Rational and Continuous Measurement of Emotional-Fingerprint, Emotional-Quotient and Categorical <i>vs </i>Proportional Recognition of Facial Emotions with M.A.R.I.E., Second Half

Granato,

Vinekar,

Gansberghe

et al. 2024

OJPsych

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Numerical Analysis of Modeling Based on Improved Elman Neural Network

Cited by 3 publications

References 19 publications

Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic Data‐Driven Modeling in Nonlinear Dynamic Power Systems

Association between maternal hepatitis B virus carrier and gestational diabetes mellitus: a retrospective cohort analysis

Rational and Continuous Measurement of Emotional-Fingerprint, Emotional-Quotient and Categorical <i>vs </i>Proportional Recognition of Facial Emotions with M.A.R.I.E., Second Half

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