Original Contribution: The CMAC and a theorem of Kolmogorov
Neural Networks
A numerical implementation of Kolmogorov's superpositions
Neural Networks
A numerical implementation of Komogorov's superpositions II
Neural Networks
Kolmogorov's theorem and its impact on soft computing
The ordered weighted averaging operators
Using kolmogorov inspired gates for low power nanoelectronics
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
| Hi-index | 0.00 |
The Kolmogorov theorem gives that the representation of continuous and bounded real-valued functions of n variables by the superposition of functions of one variable and addition is always possible. Based on the fact that each proof of the Kolmogorov theorem or its variants was a constructive one so far, there is the principal possibility to attain such a representation. This paper reviews a procedure for obtaining the Kolmogorov representation of a function, based on an approach given by David Sprecher. The construction is considered in more detail for an image function.