| Full text | |
| Author(s): |
Oliveira, Angelica Lourenco
;
Valle, Marcos Eduardo
;
Xavier-Junior, JC
;
Rios, RA
Total Authors: 4
|
| Document type: | Journal article |
| Source: | INTELLIGENT SYSTEMS, PT II; v. 13654, p. 14-pg., 2022-01-01. |
| Abstract | |
This paper presents a hybrid morphological neural network for regression tasks called linear dilation-erosion regressor (l-DER). An l-DER is given by a convex combination of the composition of linear and morphological operators. They yield continuous piecewise linear functions and, thus, are universal approximators. Besides introducing the l-DER model, we formulate their training as a difference of convex (DC) programming problem. Precisely, an l-DER is trained by minimizing the least-squares using the convex-concave procedure (CCP). Computational experiments using several regression tasks confirm the efficacy of the proposed regressor, outperforming other hybrid morphological models and state-of-the-art approaches such as the multilayer perceptron network and the radial-basis support vector regressor. (AU) | |
| FAPESP's process: | 22/01831-2 - Hypercomplex-valued neural networks: beyond complex numbers and quaternions |
| Grantee: | Marcos Eduardo Ribeiro Do Valle Mesquita |
| Support Opportunities: | Regular Research Grants |