Hybrid fuzzy morphological/linear perceptrons based on extreme learning with appli...
Automatic synthesis of arbitrarily connected artificial neural networks
MACHINE LEARNING MODELS FOR EARLY FORECAST OF SUGARCANE YIELD FOR THE MAIN PRODUCI...
Total Authors: 2
 Univ Estadual Campinas, Dept Appl Math, BR-13083859 Campinas, SP - Brazil
 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|
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|
|Support Opportunities:||Regular Research Grants|
|FAPESP's process:||17/10224-4 - Hybrid fuzzy morphological/linear perceptrons based on extreme learning with application in classification|
|Support Opportunities:||Scholarships in Brazil - Scientific Initiation|