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

arXiv: https://arxiv.org/abs/2203.11316 · HTML: https://mlsanity.com/paper/2203.11316/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 252 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [All you need is a good init](https://mlsanity.com/paper/1511.06422/) (2015)
- [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Deep Randomized Neural Networks](https://mlsanity.com/paper/2002.12287/) (2020)
- [Random Vector Functional Link Networks for Function Approximation on Manifolds](https://mlsanity.com/paper/2007.15776/) (2020)
- [Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review](https://mlsanity.com/paper/2002.09227/) (2020)
- [Deep learning in bioinformatics: introduction, application, and perspective in big data era](https://mlsanity.com/paper/1903.00342/) (2019)
- [A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+](https://mlsanity.com/paper/1708.08282/) (2017)
- [Random vector functional link neural network based ensemble deep learning for short-term load forecasting](https://mlsanity.com/paper/2107.14385/) (2021)
- [Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification](https://mlsanity.com/paper/1910.01858/) (2019)
- [Parsimonious Random Vector Functional Link Network for Data Streams](https://mlsanity.com/paper/1704.02789/) (2017)
- [Cluster-based Input Weight Initialization for Echo State Networks](https://mlsanity.com/paper/2103.04710/) (2021)
- [Weight Initialization of Deep Neural Networks(DNNs) using Data Statistics](https://mlsanity.com/paper/1710.10570/) (2017)
- [Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification](https://mlsanity.com/paper/2201.05809/) (2022)
- [Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces](https://mlsanity.com/paper/1802.01240/) (2018)
- [Are Direct Links Necessary in RVFL NNs for Regression?](https://mlsanity.com/paper/2003.13090/) (2020)
- [Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives](https://mlsanity.com/paper/2103.14624/) (2021)

## Built on by (most cited first, 30 of 33)

- [Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning](https://mlsanity.com/paper/2307.07881/) (2023)
- [Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers](https://mlsanity.com/paper/2307.08713/) (2023)
- [Multiview Random Vector Functional Link Network for Predicting DNA-Binding Proteins](https://mlsanity.com/paper/2409.02588/) (2024)
- [LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning](https://mlsanity.com/paper/2410.00645/) (2024)
- [Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function](https://mlsanity.com/paper/2408.02824/) (2024)
- [GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing](https://mlsanity.com/paper/2409.16735/) (2024)
- [Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern](https://mlsanity.com/paper/2405.07070/) (2024)
- [Solving Partial Differential Equations with Random Feature Models](https://mlsanity.com/paper/2501.00288/) (2024)
- [Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System](https://mlsanity.com/paper/2406.00801/) (2024)
- [GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning](https://mlsanity.com/paper/2409.04743/) (2024)
- [Advancing RVFL networks: Robust classification with the HawkEye loss function](https://mlsanity.com/paper/2410.00510/) (2024)
- [Comparison of Reservoir Computing topologies using the Recurrent Kernel approach](https://mlsanity.com/paper/2401.14557/) (2024)
- [Enhanced Feature Based Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.05786/) (2024)
- [Randomized based restricted kernel machine for hyperspectral image classification](https://mlsanity.com/paper/2503.05837/) (2025)
- [How deep is your network? Deep vs. shallow learning of transfer operators](https://mlsanity.com/paper/2509.19930/) (2025)
- [RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets](https://mlsanity.com/paper/2510.06278/) (2025)
- [Efficient uniform approximation using Random Vector Functional Link networks](https://mlsanity.com/paper/2306.17501/) (2023)
- [Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift](https://mlsanity.com/paper/2506.08063/) (2025)
- [Semi-adaptive Synergetic Two-way Pseudoinverse Learning System](https://mlsanity.com/paper/2406.18931/) (2024)
- [Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient](https://mlsanity.com/paper/2409.15393/) (2024)
- [HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation](https://mlsanity.com/paper/2501.18199/) (2025)
- [R^2VFL: A Robust Random Vector Functional Link Network with Huber-Weighted Framework](https://mlsanity.com/paper/2504.21069/) (2025)
- [Optimization of randomized neural networks for transfer operator approximation](https://mlsanity.com/paper/2605.23689/) (2026)
- [Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning](https://mlsanity.com/paper/2607.05635/) (2026)
- [Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework](https://mlsanity.com/paper/2607.16728/) (2026)
- [XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss](https://mlsanity.com/paper/2607.23149/) (2026)
- [Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification](https://mlsanity.com/paper/2608.10007/) (2026)
- [Robust Dual-Model Collaborative Random Vector Functional Link Network](https://mlsanity.com/paper/2608.13628/) (2026)
- [RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network](https://mlsanity.com/paper/2608.16965/) (2026)
- [Residual-Guided Randomized Neural Networks](https://mlsanity.com/paper/2608.28267/) (2026)

## Abstract (arXiv)

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 give potential future research directions/opportunities that can inspire the researchers to improve the RVFL's architecture and learning algorithm further.