| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [1] Univ Estadual Campinas, Dept Appl Math, BR-13083859 Campinas, SP - Brazil
[2] Parque Tecnol Unicamp, NeuralMind, Av Alan Turing 345, Sala 5, BR-13083898 Campinas, SP - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | NEURAL NETWORKS; v. 123, p. 288-298, MAR 2020. |
| Web of Science Citations: | 1 |
| Abstract | |
Morphological neural networks (MNNs) can be characterized as a class of artificial neural networks that perform an operation of mathematical morphology at every node, possibly followed by the application of an activation function. Morphological perceptrons (MPs) and (gray-scale) morphological associative memories are among the most widely known MNN models. Since their neuronal aggregation functions are not differentiable, classical methods of non-linear optimization can in principle not be directly applied in order to train these networks. The same observation holds true for hybrid morphological/linear perceptrons and other related models. Circumventing these problems of non-differentiability, this paper introduces an extreme learning machine approach for training a hybrid morphological/linear perceptron, whose morphological components were drawn from previous MP models. We apply the resulting model to a number of well-known classification problems from the literature and compare the performance of our model with the ones of several related models, including some recent MNNs and hybrid morphological/linear neural networks. (C) 2019 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 18/13657-1 - Some lattice computing approaches towards computational intelligence, image processing and analysis |
| Grantee: | Peter Sussner |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/10224-4 - Hybrid fuzzy morphological/linear perceptrons based on extreme learning with application in classification |
| Grantee: | Israel Campiotti |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |