Random Vector Functional Link Networks for Road Traffic Forecasting: Performance Comparison and Stability Analysis

Manibardo, Eric L.; Laña, Ibai; Ser, Javier Del

doi:10.1109/ijcnn52387.2021.9533576

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Besides the above popular areas with a large need for forecasting, RVFL and its improved versions have also succeeded in various areas, such as temperature [131], landslide displacement [182], COVID-19 cases [108], travel time [146], molten iron quality [118,117], energy consumption [147], signal-to-noise ratio [185], algae missing values [112], temperature in subway station [186] and so on [176].…”

Section: Other Datamentioning

confidence: 99%

“…In [153], Negative Correlation Learning is utilized for training ensemble RVFL networks for production rate forecasting. Manibardo et al [176] applies RVFL, deep RVFL, and edRVFL to the road traffic dataset, and the hyper-parameters are determined by Bayesian optimization. Manibardo et al [176] claims that the direct link is the fundamental reason for RVFL and its variants' superiority over ELM-based models.…”

Section: Other Datamentioning

confidence: 99%

See 1 more Smart Citation

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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