# A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes

arXiv: https://arxiv.org/abs/1710.04874 · HTML: https://mlsanity.com/paper/1710.04874/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 9 of 9)

- [A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines](https://mlsanity.com/paper/2103.08042/) (2021)
- [Stochastic Configuration Machines for Industrial Artificial Intelligence](https://mlsanity.com/paper/2308.13570/) (2023)
- [Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality](https://mlsanity.com/paper/2107.01705/) (2021)
- [Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting](https://mlsanity.com/paper/2203.00980/) (2022)
- [Data-Driven Learning of Feedforward Neural Networks with Different Activation Functions](https://mlsanity.com/paper/2107.01702/) (2021)
- [Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression](https://mlsanity.com/paper/2107.01711/) (2021)
- [Interpretable Neural Networks with Random Constructive Algorithm](https://mlsanity.com/paper/2307.00185/) (2023)
- [LightGCNet: A Lightweight Geometric Constructive Neural Network for Data-Driven Soft sensors](https://mlsanity.com/paper/2312.12022/) (2023)
- [HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation](https://mlsanity.com/paper/2501.18199/) (2025)

## Abstract (arXiv)

Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications. This is due to their unique features such as very fast training and universal approximation property. In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned. Appropriate selection of the intervals from which weights and biases are selected is extremely important. This topic has not yet been sufficiently explored in the literature. In this work a method of generating random weights and biases is proposed. This method generates the parameters of the hidden nodes in such a way that nonlinear fragments of the activation functions are located in the input space regions with data and can be used to construct the surface approximating a nonlinear target function. The weights and biases are dependent on the input data range and activation function type. The proposed methods allows us to control the generalization degree of the model. These all lead to improvement in approximation performance of the network. Several experiments show very promising results.