Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8965181 | Neurocomputing | 2018 | 8 Pages |
Abstract
We algorithmically construct a two hidden layer feedforward neural network (TLFN) model with the weights fixed as the unit coordinate vectors of the d-dimensional Euclidean space and having 3d+2 number of hidden neurons in total, which can approximate any continuous d-variable function with an arbitrary precision. This result, in particular, shows an advantage of the TLFN model over the single hidden layer feedforward neural network (SLFN) model, since SLFNs with fixed weights do not have the capability of approximating multivariate functions.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Namig J. Guliyev, Vugar E. Ismailov,