论文《Stochastic Configuration Networks: Fundamentals and Algorithms》摘要:
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.
原文链接:
https://www.researchgate.net/publication/313642491_Stochastic_Configuration_Networks_Fundamentals_and_Algorithms