Smoothing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> Regularization for Extreme Learning Machine

Fan, Qinwei; Liu, Ting

doi:10.1155/2020/9175106

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Yang et al, proposed an ETLM algorithm with a smoothing regularizer for improving the compactness of the network and confirmed experimentally that the proposed algorithm possessed better functions in prediction and network sparsity [11]. Fan et al, suggested a new efficient limit learning ML algorithm with smooth L-0 regularization and experimentally demonstrated that the proposed algorithm had fewer hidden nodes and better generalization performance [12]. Zhang et al, proposed a directional algorithm for convex nonlinear second-order conic programming.…”

Section: Related Workmentioning

confidence: 96%

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Zhang,

Ren

2023

WEVJ

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Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, which cannot be measured directly. For the exact assessment of the Li-ion battery state of charge, the research proposes an extreme learning machine algorithm based on the alternating factor multiplier method with improved regularization. This method constructs a suitable online Li-ion battery state of charge prediction model using the alternating factor multiplier method in gradient form. The experiment demonstrates that the algorithm in the study has a reduction in the number of nodes in the implicit layer relative to the traditional extreme learning machine algorithm. The error fluctuations of the algorithm under two different excitation functions range from [−0.005, 0.005] and [0.082, 0.265]; The root mean square error of the data set in which the algorithm performs well is 1.9516 and 0.6157, respectively. The real simulation scenario created the predicted values of the state of charge in the realistic simulation scenario that fit the real value curve by 99.99%. The average and maximum errors of the proposed state of charge prediction model are the smallest compared to the long and short-term memory networks and gated cyclic units, 0.58% and 2.97%, respectively. The experiment demonstrates that the presented algorithm can reduce the computational burden while guaranteeing the state of charge model prediction.

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Section: Related Workmentioning

confidence: 96%

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Zhang,

Ren

2023

WEVJ

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“…However, the regularization term is not differentiable at the origin of coordinates, which makes theoretical analysis difficult. To overcome this defect, a smoothing technique is proposed [5,14,19,23].…”

Section: Introductionmentioning

confidence: 99%

Boundedness and Convergence Analysis of a Pi-Sigma Neural Network Based on Online Gradient Method and Sparse Optimization

Fan,

Liu,

Zhao

et al. 2024

EAJAM

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“…However, since the input parameters are generated randomly and the ELM requires a large number of hidden neurons, the amplitude of the output weight will be large when the output matrix of the hidden layer is ill, which will cause the trained model to fall into the local minimum and show the phenomenon of overfitting [21]. In [22,23], an ELM based on different regularization was proposed to effectively overcome the overfitting phenomenon. e accuracy and effectiveness of the ELM algorithm largely rest with the internal parameters of the model.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

Fan

2021

Journal of Mathematics

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Extreme learning machine (ELM), as a new simple feedforward neural network learning algorithm, has been extensively used in practical applications because of its good generalization performance and fast learning speed. However, the standard ELM requires more hidden nodes in the application due to the random assignment of hidden layer parameters, which in turn has disadvantages such as poorly hidden layer sparsity, low adjustment ability, and complex network structure. In this paper, we propose a hybrid ELM algorithm based on the bat and cuckoo search algorithm to optimize the input weight and threshold of the ELM algorithm. We test the numerical experimental performance of function approximation and classification problems under a few benchmark datasets; simulation results show that the proposed algorithm can obtain significantly better prediction accuracy compared to similar algorithms.

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Smoothing L0 Regularization for Extreme Learning Machine

Cited by 9 publications

References 28 publications

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Boundedness and Convergence Analysis of a Pi-Sigma Neural Network Based on Online Gradient Method and Sparse Optimization

A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

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