Recursive least mean<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si85.gif" display="inline" overflow="scroll"><mml:mi>p</mml:mi></mml:math>-power Extreme Learning Machine

Yang, Jing; Feng, Yanghe; Rong, Hai-Jun; Chen, Badong

doi:10.1016/j.neunet.2017.04.001

Cited by 12 publications

(2 citation statements)

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“…To handle this issue, the OS-ELM with forgetting mechanism (FOS-ELM) was proposed by Zhao et al [11], where the forgetting mechanism can discard obsolescence samples and enhance the accuracy of the predictive model. Zou et al [12] proposed the memory degradation based OS-ELM (MDOS-ELM) which adjusts the weights of the old and new samples by a self-adaptive memory factor, and discards invalid samples. Generally, FOS-ELM models built on samples without noise or only with the Gaussian noise can obtain satisfactory precisions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise

2023

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Samples collected from most industrial processes have two challenges: one is contaminated by the non-Gaussian noise, and the other is gradually obsolesced. This feature can obviously reduce the accuracy and generalization of models. To handle these challenges, a novel method, named the robust online extreme learning machine (RO-ELM), is proposed in this paper, in which the least mean -power criterion is employed as the cost function which is to boost the robustness of the ELM, and the forgetting mechanism is introduced to discard the obsolescence samples. To investigate the performance of the RO-ELM, experiments on artificial and real-world datasets with the non-Gaussian noise are performed, and the datasets are from regression or classification problems. Results show that the RO-ELM is more robust than the ELM, the online sequential ELM (OS-ELM) and the OS-ELM with forgetting mechanism (FOS-ELM). The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.

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Section: Introductionmentioning

confidence: 99%

A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise

2023

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“…From the aspect of the optimization method, the stochastic gradient descent (SGD)-based algorithms cannot find the minimum using the negative gradient in some loss functions [20][21][22]. Toward this end, recursive-based algorithms [23] address these issues at the cost of increasing computational cost. In comparison with the SGD method and recursive method, the conjugate gradient (CG) method [24][25][26] and Newton's method as developments of SGD have become alternative optimization methods in KAFs.…”

Section: Introductionmentioning

confidence: 99%

The Cauchy Conjugate Gradient Algorithm with Random Fourier Features

Huang

Wang²,

Xiong³

2019

Symmetry

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Random Fourier mapping (RFM) in kernel adaptive filters (KAFs) provides an efficient method to curb the linear growth of the dictionary by projecting the original input data into a finite-dimensional space. The commonly used measure in RFM-based KAFs is the minimum mean square error (MMSE), which causes performance deterioration in the presence of non-Gaussian noises. To address this issue, the minimum Cauchy loss (MCL) criterion has been successfully applied for combating non-Gaussian noises in KAFs. However, these KAFs using the well-known stochastic gradient descent (SGD) optimization method may suffer from slow convergence rate and low filtering accuracy. To this end, we propose a novel robust random Fourier features Cauchy conjugate gradient (RFFCCG) algorithm using the conjugate gradient (CG) optimization method in this paper. The proposed RFFCCG algorithm with low complexity can achieve better filtering performance than the KAFs with sparsification, such as the kernel recursive maximum correntropy algorithm with novelty criterion (KRMC-NC), in stationary and non-stationary environments. Monte Carlo simulations conducted in the time-series prediction and nonlinear system identification confirm the superiorities of the proposed algorithm.

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