Correction to: Deep kernel learning in extreme learning machines

Afzal, A. L.; Nair, Nikhitha K.; Asharaf, S.

doi:10.1007/s10044-020-00944-y

Cited by 2 publications

(1 citation statement)

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“…ELM model is a classical feedforward neural network. Its excellent fast learning ability make it widely used in the fields of fitting regression of nonlinear functions [28][29][30][31]. Compared with the traditional neural network does not need to set learning objectives, learning rates, and other complex parameters.…”

Section: Extreme Learning Machine(elm)mentioning

confidence: 99%

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

Yuan

Liang

Jiawei

et al. 2022

Preprint

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Aiming at the problem of low accuracy and unstable prediction results caused by the random connection weight threshold between the input layer and the hidden layer of extreme learning machine (ELM). An adaptive dual-strategy improved pelican optimization algorithm (IPOA) -ELM regression prediction model is proposed. Firstly, the pelican optimization algorithm (POA) is improved by the Logistic-Tent chaotic map, improved convergence factor by adaptive double strategy method and reverse learning strategy. Then, the performance of IPOA is verified by multiple groups of multi-dimensional single-peak and multi-peak test functions. The test results show that IPOA has better accuracy, stability, and robustness than POA, butterfly optimization algorithm (BOA), cuckoo algorithm (CS), grey wolf algorithm (GWO), particle swarm optimization algorithm (PSO), genetic algorithm (GA) and mouse swarm optimization algorithm (RSO). Finally, IPOA is applied to optimize ELM, and the performance of the IPOA-ELM model is verified by three engineering data sets. The simulation results show that the convergence accuracy, stability, and robustness of the IPOA-ELM model are better than those of the POA-ELM and ELM models.

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Section: Extreme Learning Machine(elm)mentioning

confidence: 99%

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

Yuan

Liang

Jiawei

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

et al. 2022

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The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky–Golay (S–G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R 2 , root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.

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Correction to: Deep kernel learning in extreme learning machines

Cited by 2 publications

References 0 publications

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

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