Morphological perceptrons with competitive learning: Lattice-theoretical framework and constructive learning algorithm
A morphological neural network is generally defined as a type of artificial neural network that
performs an elementary operation of mathematical morphology at every node, possibly …
performs an elementary operation of mathematical morphology at every node, possibly …
An overview of some classical growing neural networks and new developments
X Qiang, G Cheng, Z Wang - 2010 2nd International …, 2010 - ieeexplore.ieee.org
The map** capability of artificial neural networks (ANN) is dependent on their structure, ie,
the number of layers and the number of hidden units. There is no formal way of computing …
the number of layers and the number of hidden units. There is no formal way of computing …
Differential evolution training algorithm for dendrite morphological neural networks
Dendrite morphological neural networks are emerging as an attractive alternative for pattern
classification, providing competitive results with other classification methods. A key problem …
classification, providing competitive results with other classification methods. A key problem …
Extreme learning machine for a new hybrid morphological/linear perceptron
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 …
networks that perform an operation of mathematical morphology at every node, possibly …
Discrete morphological neural networks
A classical approach to designing binary image operators is mathematical morphology
(MM). We propose the Discrete Morphological Neural Networks (DMNN) for binary image …
(MM). We propose the Discrete Morphological Neural Networks (DMNN) for binary image …
Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals' numbers (INs)
Linear models are preferable due to simplicity. Nevertheless, non-linear models often
emerge in practice. A popular approach for modeling nonlinearities is by piecewise-linear …
emerge in practice. A popular approach for modeling nonlinearities is by piecewise-linear …
Dendrite ellipsoidal neurons based on k-means optimization
Dendrite morphological neurons are a type of artificial neural network that can be used to
solve classification problems. The major difference with respect to classical perceptrons is …
solve classification problems. The major difference with respect to classical perceptrons is …
Bipolar morphological neural networks: convolution without multiplication
In the paper we introduce a novel bipolar morphological neuron and bipolar morphological
layer models. The models use only such operations as addition, subtraction and maximum …
layer models. The models use only such operations as addition, subtraction and maximum …
Swarm-based translation-invariant morphological prediction method for financial time series forecasting
RA Araújo - Information Sciences, 2010 - Elsevier
In this paper, we present a method to overcome the random walk (RW) dilemma for financial
time series forecasting, called swarm-based translation-invariant morphological prediction …
time series forecasting, called swarm-based translation-invariant morphological prediction …
On machine-learning morphological image operators
Morphological operators are nonlinear transformations commonly used in image
processing. Their theoretical foundation is based on lattice theory, and it is a well-known …
processing. Their theoretical foundation is based on lattice theory, and it is a well-known …