# Stochastic Configuration Networks: Fundamentals and Algorithms

## TL;DR

This paper introduces Stochastic Configuration Networks (SCNs), a new randomized neural network method that incrementally builds models with supervisory-guided randomization, offering fast learning and good generalization for regression and classification tasks.

## Contribution

It proposes a novel stochastic configuration approach for neural networks, establishing their universal approximation property and presenting three algorithms for regression and classification.

## Key findings

- SCNs demonstrate fast learning and good generalization.
- They require less human intervention in network size setting.
- Simulation results show superior performance in function approximation and real data regression.

## Abstract

This paper contributes to a development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed as Stochastic Configuration Networks (SCNs). In contrast to the existing randomised learning algorithms for single layer feed-forward neural networks (SLFNNs), we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either constructive or selective manner. As fundamentals of SCN-based data modelling techniques, we establish some theoretical results on the universal approximation property. Three versions of SC algorithms are presented for regression problems (applicable for classification problems as well) in this work. Simulation results concerning both function approximation and real world data regression indicate some remarkable merits of our proposed SCNs in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning and sound generalization.