A robust extreme learning machine for modeling a small-scale turbojet engine

Zhao, Yong-Ping; Hu, Qian-Kun; Xu, Jinming; Li, Bing; Huang, Gong; Pan, Ying-Ting

doi:10.1016/j.apenergy.2018.02.175

Cited by 34 publications

(3 citation statements)

References 31 publications

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“…By randomly generating the weights

w_i

and the biases

e_i

, the output weight is determined as follows 35 :

\begin{equation} \hat{\alpha }= {G^ + }H \end{equation}

where

{G^ + }

represents the Moore–Penrose generalized inverse of matrix

G

and

{G^ + } = {({G^T}G)^{ - 1}}{G^T}

.…”

Section: Methodologiesmentioning

confidence: 99%

See 1 more Smart Citation

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

Zheng,

Wu,

Tao

2024

Quality & Reliability Eng

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As one of the key technologies in battery management system, accurate remaining useful life (RUL) prediction is critical to guarantee the reliability and safety for electrical equipment. However, the generalization and robustness of a single method are limited. A novel fusion data‐driven RUL prediction method CSSA‐ELM‐LSSVR based on charging‐discharging health features extraction is proposed in this paper, which fusions chaotic sparrow search algorithm (CSSA), extreme learning machine (ELM), and least squares support vector regression (LSSVR). First, four health indicators (HIs) are extracted from the charging‐discharging process, which can reflect the battery degradation phenomenon from multiple perspectives. Then, pearson correlation coefficient is used to numerically analyze the correlation between HIs and battery aging capacities. Second, the extracted HIs are used as the inputs for ELM and LSSVR to predict the degradation trend of battery, where CSSA is used for hyperparameters optimization in ELM. Finally, considering that CSSA‐ELM can capture the general trend of degradation curves, while LSSVR can trace the detail changes, a fusion framework based on CSSA‐ELM and LSSVR is proposed for RUL prediction. Two weighting schemes, namely precision‐based weighting (PW) and random forest regressor‐based weighting (RFRW) are put forward to fix the weights of CSSA‐ELM and LSSVR algorithms. Two publicly available datasets from National Aeronautics and Space Administration (NASA) and MIT are adopted to verify the feasibility and effectiveness of the proposed method. The results indicate that the proposed method with any weighting scheme has an overall superior prediction performance for different kinds of batteries compared with CSSA‐ELM, LSSVR, convolution neural network and long short term memory. Moreover, the RFRW scheme has better overall performance. Specifically, the maximum root mean square error of the predicted method is 2.5126%, the mean absolute percentage error is 12.9167%, the mean absolute error is 1.8376%, the predicted RUL errors are within one cycle, and the determination coefficient R2 is above 0.97.

show abstract

“…By randomly generating the weights

w_i

and the biases

e_i

, the output weight is determined as follows 35 :

\begin{equation} \hat{\alpha }= {G^ + }H \end{equation}

where

{G^ + }

represents the Moore–Penrose generalized inverse of matrix

G

and

{G^ + } = {({G^T}G)^{ - 1}}{G^T}

.…”

Section: Methodologiesmentioning

confidence: 99%

“…By randomly generating the weights 𝑤 𝑖 and the biases 𝑒 𝑖 , the output weight is determined as follows 35 :…”

Section: Elm Neural Networkmentioning

confidence: 99%

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

Zheng,

Wu,

Tao

2024

Quality & Reliability Eng

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show abstract

“…The standard mathematical model of RELM is represented as follows: L norm is used in the coding stage, while the output weights are derived by the Lagrange multiplier method. Zhao et al [24] proposed the Robust ELM algorithm (RRELM) by introducing both the deviation and variance of the model into the objective function for optimization and keeping the 2 L penalty term constant. RRELM considers both the variance and deviation of the model and seeks to achieve the best compromise between them to enhance the network generalization performance and robustness.…”

Section: Related Workmentioning

confidence: 99%

An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine

Tang

2021

IEEE Access

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Extreme learning machine (ELM) is a novel single-hidden layer feedforward neural network to obtain fast learning speed by randomly initializing weights and deviations. Due to its extremely fast learning speed, it has been widely used in training of massive data in recent years. In order to adapt to the real network environment, based on the ELM, we propose an improved particle swarm optimized online regularized extreme learning machine (IPSO-IRELM) intrusion detection algorithm model. First, the model replaces the traditional batch learning with sequential learning by dynamically adapting the new data obtained in the training network instead of training all collected samples in an offline manner; second, we improve the particle swarm optimization algorithm and compare it with typical improved algorithms to prove its effectiveness; finally, to solve the random initialization problem of IRELM, we use IPSO to optimize the initial weights and deviations of IRELM to improve the classification ability of IRELM. The experimental results show that IPSO-IRELM algorithm has better generalization ability, which not only improves the accuracy of intrusion detection, but also has certain recognition ability for minority class samples.

show abstract

Thermodynamic-based analyses and assessments of a new-generation turbojet engine used for unmanned aerial vehicles (UAVs)

Şöhret

Çalışkan

2022

J Therm Anal Calorim

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A robust extreme learning machine for modeling a small-scale turbojet engine

Cited by 34 publications

References 31 publications

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine

Thermodynamic-based analyses and assessments of a new-generation turbojet engine used for unmanned aerial vehicles (UAVs)

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