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

• Published in: Applied soft computing Vol. 143; p. 110377
• Main Authors: Malik, A.K., Gao, Ruobin, Ganaie, M.A., Tanveer, M., Suganthan, Ponnuthurai Nagaratnam
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: Deep learning; Ensemble deep learning; Ensemble learning; Random vector functional link (RVFL) network; Randomized neural networks (RNNs), Single hidden layer feed forward neural network (SLFN), Extreme learning machine (ELM); Deep learning; Ensemble deep learning; Random vector functional link (RVFL) network; Ensemble learning; Randomized neural networks (RNNs), Single hidden layer feed forward neural network (SLFN), Extreme learning machine (ELM)
• ISSN: 1568-4946
• DOI: 10.1016/j.asoc.2023.110377

Neural networks have been successfully employed in various domains 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, direct links, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the first comprehensive review of the evolution of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFLs, ensemble RVFLs, deep RVFLs 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 present potential future research directions/opportunities that can inspire the researchers to improve the RVFL’s architecture and learning algorithm further. •The first survey focusing solely on RVFL-based models.•RVFLs in shallow, ensemble, deep, and ensemble deep frameworks have been discussed.•Various applications of the RVFL have been discussed.•Hyper-parameter optimization and experimental setups for the RVFL are discussed.•We present potential future research directions for the RVFL model.