Implementation of Kolmogorov Learning Algorithm for Feedforward Neural Networks

Authors:
Roman Neruda;Arnost Stedrý;Jitka Drkosová
Affiliations:
-;-;-
Venue:
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Year:
2001

Citing 6
Cited 1

Kolmogorov's theorem and multilayer neural networks

Neural Networks
Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Neural Networks
A numerical implementation of Komogorov's superpositions II

Neural Networks
Kolmogorov's theorem is relevant

Neural Computation
Representation properties of networks: Kolmogorov's theorem is irrelevant

Neural Computation
A universal mapping for kolmogorov's superposition theorem

Neural Networks

Variants of Learning Algorithm Based on Kolmogorov Theorem

ICCS '02 Proceedings of the International Conference on Computational Science-Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a learning algorithm for feedforward neural networks that is based on Kolmogorov theorem concerning composition of n- dimensional continuous function from one-dimensional continuous functions. A thorough analysis of the algorithm time complexity is presented together with serial and parallel implementation examples.