Why mathematical morphology needs complete lattices
Signal Processing
The handbook of brain theory and neural networks
LADAR target detection using morphological shared-weighted neural networks
Machine Vision and Applications
Fuzzy lattice neurocomputing (FLN) models
Neural Networks
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
An Introduction to Morphological Neural Networks
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Journal of Mathematical Imaging and Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
A general framework for fuzzy morphological associative memories
Fuzzy Sets and Systems
Implicative Fuzzy Associative Memories
IEEE Transactions on Fuzzy Systems
Fuzzy lattice neural network (FLNN): a hybrid model for learning
IEEE Transactions on Neural Networks
Automatic target detection using entropy optimized shared-weight neural networks
IEEE Transactions on Neural Networks
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Information Sciences: an International Journal
FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices
Computers & Mathematics with Applications
Information Sciences: an International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Hybrid morphological methodology for software development cost estimation
Expert Systems with Applications: An International Journal
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Fuzzy lattice reasoning for pattern classification using a new positive valuation function
Advances in Fuzzy Systems
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Morphological neural networks grew out of a merger of ideas from artificial neural networks and mathematical morphology. The morphological perceptron, one of the first morphological neural networks that appeared in the literature, was originally developed as a simple model for solving binary classification problems. Until recently, the morphological perceptron has not received much attention due to its simplicity and limited applicability. In this paper, we introduce a new version of the morphological perceptron called morphological perceptron with competitive learning including an appropriate algorithm for training this model. Instead of a single binary output neuron like the original morphological perceptron, the new model as a winner-take-all output layer and the decision surface after training does not depend on the order in which the patterns are presented to the network. Finally, the paper includes some experimental results on two well-known datasets that indicate the utility of the morphological perceptron with competitive learning in classification problems.