A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Why mathematical morphology needs complete lattices
Signal Processing
Time series and dependent variables
Physica D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
An Introduction to Morphological Neural Networks
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Neural Computing and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Morphological/rank neural networks and their adaptive optimal design for image processing
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding the embedding dimension and variable dependencies in time series
Neural Computation
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Discriminative Learning for Dynamic State Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Adaptive numerical algorithms in space weather modeling
Journal of Computational Physics
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Morphological associative memories
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Multifeedback-Layer Neural Network
IEEE Transactions on Neural Networks
Support Vector Echo-State Machine for Chaotic Time-Series Prediction
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
A Morphological-Rank-Linear evolutionary method for stock market prediction
Information Sciences: an International Journal
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The weather forecasting is considered a rather difficult problem due to many complex features present in these time series. Several techniques have been proposed in the literature to solve this problem. In particular, the dilation-erosion perceptron (DEP), a model whose foundations are based on mathematical morphology and complete lattice theory, has been successfully used for time series forecasting. However, a drawback arises from the gradient estimation of morphological operators in the classical gradient-based learning process of the DEP, since they are not differentiable of usual way. In this sense, this work presents evolutionary learning processes, using a modified genetic algorithm, a particle swarm optimization, a modified differential evolution and a covariance matrix adaptation evolutionary strategy, to design the DEP model for weather forecasting. In addition, into the proposed learning processes we have included an automatic correction step that is geared toward eliminating time phase distortions that occur in some weather phenomena. An experimental analysis is presented using three non-linear forecasting problems from the Brazilian weather, and the obtained results are discussed and compared, according to five well-known performance metrics and an evaluation function, to results found using the DEP model with its classical gradient-based learning process.